You searched for subject:(Supervised classification).
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Colorado School of Mines
1.
Jackson, Ryan Blake.
Machine learning for encrypted Amazon Echo traffic classification.
Degree: MS(M.S.), Computer Science, 2018, Colorado School of Mines
URL: http://hdl.handle.net/11124/172223 
► As smart speakers like the Amazon Echo become more popular, they have given rise to rampant concerns regarding user privacy. This work investigates machine learning…
(more)
▼ As smart speakers like the Amazon Echo become more popular, they have given rise to rampant concerns regarding user privacy. This work investigates machine learning techniques to extract ostensibly private information from the TCP traffic moving between an Echo device and Amazon's servers, despite the fact that all such traffic is encrypted. Specifically, we investigate two
supervised classification problems using six machine learning algorithms and three feature vectors. The "request type
classification" problem seeks to determine what type of user request is being answered by the Echo. With six classes, we achieve 97% accuracy in this task using random forests. The "speaker identification" problem seeks to determine who, of a finite set of possible speakers, is speaking to the Echo. In this task, with two classes, we outperform random guessing by a small but statistically significant margin with an accuracy of 58%. We discuss the reasons for, and implications of, these results, and suggest several avenues for future research in this domain.
Advisors/Committee Members: Camp, Tracy (advisor), Wang, Hua (committee member), Schurgot, Mary (committee member).
Subjects/Keywords: supervised classification; machine learning
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APA (6th Edition):
Jackson, R. B. (2018). Machine learning for encrypted Amazon Echo traffic classification. (Masters Thesis). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/172223
Chicago Manual of Style (16th Edition):
Jackson, Ryan Blake. “Machine learning for encrypted Amazon Echo traffic classification.” 2018. Masters Thesis, Colorado School of Mines. Accessed January 23, 2021.
http://hdl.handle.net/11124/172223.
MLA Handbook (7th Edition):
Jackson, Ryan Blake. “Machine learning for encrypted Amazon Echo traffic classification.” 2018. Web. 23 Jan 2021.
Vancouver:
Jackson RB. Machine learning for encrypted Amazon Echo traffic classification. [Internet] [Masters thesis]. Colorado School of Mines; 2018. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/11124/172223.
Council of Science Editors:
Jackson RB. Machine learning for encrypted Amazon Echo traffic classification. [Masters Thesis]. Colorado School of Mines; 2018. Available from: http://hdl.handle.net/11124/172223
McMaster University
2.
Ateeq, Sameen.
Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.
Degree: MSc, 2018, McMaster University
URL: http://hdl.handle.net/11375/24095
► According to the Public Health Agency of Canada, falls account for 95% of all hip fractures in Canada; 20% of fall-related injury cases end in…
(more)
▼ According to the Public Health Agency of Canada, falls account for 95% of all hip fractures in Canada; 20% of fall-related injury cases end in death. This thesis evaluates the predictive power of many variables to predict fall-related injuries. The dataset chosen was CCHS which is high dimensional and diverse. The use of Principal Component Analysis (PCA) and random forest was employed to determine the highest priority risk factors to include in the predictive model. The results show that it is possible to predict fall-related injuries with a sensitivity of 80% or higher using four predictors (frequency of consultations with medical doctor, food and vegetable consumption, height and monthly physical activity level of over 15 minutes). Alternatively, the same sensitivity can be reached using age, frequency of walking for exercise per 3 months, alcohol consumption and personal income. None of the predictive models reached an accuracy of 70% or higher.
Further work in studying nutritional diets that offer protection from incurring a fall related injury are also recommended. Since the predictors are behavioral determinants of health and have a high sensitivity but a low accuracy, population health interventions are recommended rather than individual-level interventions. Suggestions to improve accuracy of built models are also proposed.
Thesis
Master of Science (MSc)
Advisors/Committee Members: Samavi, Reza, eHealth.
Subjects/Keywords: machine learning; supervised classification; falls; CCHS; injuries
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APA (6th Edition):
Ateeq, S. (2018). Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/24095
Chicago Manual of Style (16th Edition):
Ateeq, Sameen. “Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.” 2018. Masters Thesis, McMaster University. Accessed January 23, 2021.
http://hdl.handle.net/11375/24095.
MLA Handbook (7th Edition):
Ateeq, Sameen. “Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.” 2018. Web. 23 Jan 2021.
Vancouver:
Ateeq S. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/11375/24095.
Council of Science Editors:
Ateeq S. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/24095
University of Manchester
3.
Aguilar Ariza, Andres.
MACHINE LEARNING AND BIG DATA TECHNIQUES FOR
SATELLITE-BASED RICE PHENOLOGY MONITORING.
Degree: 2019, University of Manchester
URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:320587
► New sources of information are required to support rice production decisions. To cope with this challenge, studies have found practical applications on mapping rice through…
(more)
▼ New sources of information are required to support
rice production decisions. To cope with this challenge, studies
have found practical applications on mapping rice through the use
of remote sensing techniques. This study attempts to implement a
methodology aimed at mon- itoring rice phenology using optical
satellite data. The relationship between rice phenology and
reflectance metrics was explored at two levels: growth stages and
biophysical modifications caused by diseases. Two optical
moderate-resolution missions were combined to detect growth phases.
Three machine learning approaches (random forest, support vector
machine, and gra- dient boosting trees) were trained with
multitemporal NDVI data. Analytics from validation showed that the
algorithms were able to estimate rice phases with performances
above 0.94 in f-1 score. Tested models yielded an overall accuracy
of 71.8%, 71.2%, 60.9% and 94.7% for vegetative, reproductive,
ripening and harvested categories. A second exploration was carried
out by combining Sentinel-2 data and ground-based information about
rice disease incidence. K-means clustering was used to map rice
biophysical changes across reproductive and ripening phases. The
findings ascertained the remote sensing capabilities to create new
information about rice for Colombia’s
conditions.
Advisors/Committee Members: FENNELL, JOSEPH J, Bridle, Sarah, Fennell, Joseph.
Subjects/Keywords: Remote sensing; Agriculture; Rice phenology; Supervised classification
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APA (6th Edition):
Aguilar Ariza, A. (2019). MACHINE LEARNING AND BIG DATA TECHNIQUES FOR
SATELLITE-BASED RICE PHENOLOGY MONITORING. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:320587
Chicago Manual of Style (16th Edition):
Aguilar Ariza, Andres. “MACHINE LEARNING AND BIG DATA TECHNIQUES FOR
SATELLITE-BASED RICE PHENOLOGY MONITORING.” 2019. Doctoral Dissertation, University of Manchester. Accessed January 23, 2021.
http://www.manchester.ac.uk/escholar/uk-ac-man-scw:320587.
MLA Handbook (7th Edition):
Aguilar Ariza, Andres. “MACHINE LEARNING AND BIG DATA TECHNIQUES FOR
SATELLITE-BASED RICE PHENOLOGY MONITORING.” 2019. Web. 23 Jan 2021.
Vancouver:
Aguilar Ariza A. MACHINE LEARNING AND BIG DATA TECHNIQUES FOR
SATELLITE-BASED RICE PHENOLOGY MONITORING. [Internet] [Doctoral dissertation]. University of Manchester; 2019. [cited 2021 Jan 23].
Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:320587.
Council of Science Editors:
Aguilar Ariza A. MACHINE LEARNING AND BIG DATA TECHNIQUES FOR
SATELLITE-BASED RICE PHENOLOGY MONITORING. [Doctoral Dissertation]. University of Manchester; 2019. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:320587
4.
GOH JIE MEIN.
Incorporating linguistically motivated knowledge sources into document classification.
Degree: 2004, National University of Singapore
URL: http://scholarbank.nus.edu.sg/handle/10635/14044
Subjects/Keywords: supervised document classification
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APA (6th Edition):
MEIN, G. J. (2004). Incorporating linguistically motivated knowledge sources into document classification. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/14044
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
MEIN, GOH JIE. “Incorporating linguistically motivated knowledge sources into document classification.” 2004. Thesis, National University of Singapore. Accessed January 23, 2021.
http://scholarbank.nus.edu.sg/handle/10635/14044.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
MEIN, GOH JIE. “Incorporating linguistically motivated knowledge sources into document classification.” 2004. Web. 23 Jan 2021.
Vancouver:
MEIN GJ. Incorporating linguistically motivated knowledge sources into document classification. [Internet] [Thesis]. National University of Singapore; 2004. [cited 2021 Jan 23].
Available from: http://scholarbank.nus.edu.sg/handle/10635/14044.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
MEIN GJ. Incorporating linguistically motivated knowledge sources into document classification. [Thesis]. National University of Singapore; 2004. Available from: http://scholarbank.nus.edu.sg/handle/10635/14044
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Notre Dame
5.
Troy William Raeder.
Evaluating and Maintaining Classification
Algorithms</h1>.
Degree: Computer Science and Engineering, 2012, University of Notre Dame
URL: https://curate.nd.edu/show/4b29b56616h
► Any practical application of machine learning necessarily begins with the selection of a classification algorithm. Generally, practitioners will try several different types of algorithms…
(more)
▼ Any practical application of machine learning
necessarily begins with the selection of a
classification
algorithm. Generally, practitioners will try several different
types of algorithms (such as decision trees, Bayesian algorithms,
support vector machines, or neural networks) and select the
algorithm that performs best on a subset of the available data.
That is to say, some measurement of the classifier’s performance on
past data is used as an estimate of its performance on future data.
Ideally, this estimate is perfectly aligned with the extit{true
cost} of applying the classifier on future data, but this far from
guaranteed in practice. First, any estimate of classifier
performance has variance, and this variance is difficult to
estimate. Additionally, misclassification costs are rarely known at
model-selection time and the characteristics of the population from
which data are drawn may change over time. If the training-time
estimate of either misclassification cost or data distribution is
incorrect, the chosen classifier is sub-optimal and may perform
worse than expected. Finally, once a suitable classifier is built
and deployed, there need to be systems in place to ensure that it
continues to perform at a high level over time. The purpose of this
dissertation is to improve the processes of classifier evaluation,
selection, and maintenance in real-world
situations.
Advisors/Committee Members: Dr. W. Philip Kegelmeyer, Committee Member, Dr. Patrick J. Flynn, Committee Member, Dr. Nitesh V. Chawla, Committee Chair, Dr. Kevin W. Bowyer, Committee Member.
Subjects/Keywords: classification; supervised learning; evaluation; concept drift
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Manager
APA (6th Edition):
Raeder, T. W. (2012). Evaluating and Maintaining Classification
Algorithms</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/4b29b56616h
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Raeder, Troy William. “Evaluating and Maintaining Classification
Algorithms</h1>.” 2012. Thesis, University of Notre Dame. Accessed January 23, 2021.
https://curate.nd.edu/show/4b29b56616h.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Raeder, Troy William. “Evaluating and Maintaining Classification
Algorithms</h1>.” 2012. Web. 23 Jan 2021.
Vancouver:
Raeder TW. Evaluating and Maintaining Classification
Algorithms</h1>. [Internet] [Thesis]. University of Notre Dame; 2012. [cited 2021 Jan 23].
Available from: https://curate.nd.edu/show/4b29b56616h.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Raeder TW. Evaluating and Maintaining Classification
Algorithms</h1>. [Thesis]. University of Notre Dame; 2012. Available from: https://curate.nd.edu/show/4b29b56616h
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Penn State University
6.
Biyani, Prakhar.
Analyzing Subjectivity and Sentiment of Online Forums.
Degree: 2014, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/22850
► Online social media has emerged as a popular medium for seeking and providing information, opinions and social support. Online sites such as discussion forums, blogs…
(more)
▼ Online social media has emerged as a popular medium for seeking and providing information, opinions and social support. Online sites such as discussion forums, blogs and health communities have tremendous amounts of user generated data in their archives. Analyzing this content for its subjectivity and sentiment has important applications such as improving information search in social media, understanding users for providing content personalization, identifying influential members in online communities, etc. In this dissertation, I will discuss my works on subjectivity analysis of online forum threads, identifying the type of social support (emotional or informational) present in and analyzing sentiment of user messages in an online health community (OHC). For subjectivity analysis, I show that thread-specific non-lexical features such as thread structure and dialogue acts expressed in thread posts are highly informative for inferring thread subjectivity. For sentiment analysis of messages of the OHC, I use unlabeled messages to augment a small training data using co-training and build highly accurate sentiment classifiers. For support identification, I build
supervised classifiers using several generic and novel domain-specific features and analyze the posting behaviors of regular members and influential members in the OHC in terms of the type of support they provide in their messages. I find that influential members generally provide more emotional support as compared to regular members in the OHC. Experimental results demonstrate that all the proposed models significantly outperform various state-of-the-art models.
Advisors/Committee Members: Prasenjit Mitra, Dissertation Advisor/Co-Advisor, Prasenjit Mitra, Committee Chair/Co-Chair, John Yen, Committee Member, Alexander Klippel, Committee Member, Marcel Salathe, Committee Member, Cornelia Caragea, Special Member.
Subjects/Keywords: Subjectivity analysis; sentiment analysis; classification; supervised learning; semi-supervised learning; online forums
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APA ·
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Manager
APA (6th Edition):
Biyani, P. (2014). Analyzing Subjectivity and Sentiment of Online Forums. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/22850
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Biyani, Prakhar. “Analyzing Subjectivity and Sentiment of Online Forums.” 2014. Thesis, Penn State University. Accessed January 23, 2021.
https://submit-etda.libraries.psu.edu/catalog/22850.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Biyani, Prakhar. “Analyzing Subjectivity and Sentiment of Online Forums.” 2014. Web. 23 Jan 2021.
Vancouver:
Biyani P. Analyzing Subjectivity and Sentiment of Online Forums. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Jan 23].
Available from: https://submit-etda.libraries.psu.edu/catalog/22850.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Biyani P. Analyzing Subjectivity and Sentiment of Online Forums. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/22850
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
7.
Chzhen, Evgenii.
Plug-in methods in classification : Méthodes de type plug-in en classification.
Degree: Docteur es, Mathématiques, 2019, Université Paris-Est
URL: http://www.theses.fr/2019PESC2027
► Ce manuscrit étudie plusieurs problèmes de classification sous contraintes. Dans ce cadre de classification, notre objectif est de construire un algorithme qui a des performances…
(more)
▼ Ce manuscrit étudie plusieurs problèmes de classification sous contraintes. Dans ce cadre de classification, notre objectif est de construire un algorithme qui a des performances aussi bonnes que la meilleure règle de classification ayant une propriété souhaitée. Fait intéressant, les méthodes de classification de type plug-in sont bien appropriées à cet effet. De plus, il est montré que, dans plusieurs configurations, ces règles de classification peuvent exploiter des données non étiquetées, c'est-à-dire qu'elles sont construites de manière semi-supervisée. Le Chapitre 1 décrit deux cas particuliers de la classification binaire - la classification où la mesure de performance est reliée au F-score, et la classification équitable. A ces deux problèmes, des procédures semi-supervisées sont proposées. En particulier, dans le cas du F-score, il s'avère que cette méthode est optimale au sens minimax sur une classe usuelle de distributions non-paramétriques. Aussi, dans le cas de la classification équitable, la méthode proposée est consistante en terme de risque de classification, tout en satisfaisant asymptotiquement la contrainte d’égalité des chances. De plus, la procédure proposée dans ce cadre d'étude surpasse en pratique les algorithmes de pointe. Le Chapitre 3 décrit le cadre de la classification multi-classes par le biais d'ensembles de confiance. Là encore, une procédure semi-supervisée est proposée et son optimalité presque minimax est établie. Il est en outre établi qu'aucun algorithme supervisé ne peut atteindre une vitesse de convergence dite rapide. Le Chapitre 4 décrit un cas de classification multi-labels dans lequel on cherche à minimiser le taux de faux-négatifs sous réserve de contraintes de type presque sûres sur les règles de classification. Dans cette partie, deux contraintes spécifiques sont prises en compte: les classifieurs parcimonieux et ceux soumis à un contrôle des erreurs négatives à tort. Pour les premiers, un algorithme supervisé est fourni et il est montré que cet algorithme peut atteindre une vitesse de convergence rapide. Enfin, pour la seconde famille, il est montré que des hypothèses supplémentaires sont nécessaires pour obtenir des garanties théoriques sur le risque de classification
This manuscript studies several problems of constrained classification. In this frameworks of classification our goal is to construct an algorithm which performs as good as the best classifier that obeys some desired property. Plug-in type classifiers are well suited to achieve this goal. Interestingly, it is shown that in several setups these classifiers can leverage unlabeled data, that is, they are constructed in a semi-supervised manner.Chapter 2 describes two particular settings of binary classification – classification with F-score and classification of equal opportunity. For both problems semi-supervised procedures are proposed and their theoretical properties are established. In the case of the F-score, the proposed procedure is shown to be optimal in minimax sense over a standard…
Advisors/Committee Members: Merlevède, Florence (thesis director), Salmon, Joseph (thesis director).
Subjects/Keywords: Classification contrainte; Classification supervisée; Classification semi-Supervisée; Analyse minimax; Classification par plug-In; Ensembles de confiance; Constrained classification; Supervised classification; Semi-Supervised classification; Minimax analysis; Plug-In classification; Confidence sets
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APA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Chzhen, E. (2019). Plug-in methods in classification : Méthodes de type plug-in en classification. (Doctoral Dissertation). Université Paris-Est. Retrieved from http://www.theses.fr/2019PESC2027
Chicago Manual of Style (16th Edition):
Chzhen, Evgenii. “Plug-in methods in classification : Méthodes de type plug-in en classification.” 2019. Doctoral Dissertation, Université Paris-Est. Accessed January 23, 2021.
http://www.theses.fr/2019PESC2027.
MLA Handbook (7th Edition):
Chzhen, Evgenii. “Plug-in methods in classification : Méthodes de type plug-in en classification.” 2019. Web. 23 Jan 2021.
Vancouver:
Chzhen E. Plug-in methods in classification : Méthodes de type plug-in en classification. [Internet] [Doctoral dissertation]. Université Paris-Est; 2019. [cited 2021 Jan 23].
Available from: http://www.theses.fr/2019PESC2027.
Council of Science Editors:
Chzhen E. Plug-in methods in classification : Méthodes de type plug-in en classification. [Doctoral Dissertation]. Université Paris-Est; 2019. Available from: http://www.theses.fr/2019PESC2027
8.
Lehaire, Jérôme.
Détection et caractérisation du cancer de la prostate par images IRM 1.5T multiparamétriques : Computer-aided decision system for prostate cancer detection and characterization based on multi-parametric 1.5T MRI.
Degree: Docteur es, Traitement d'images, 2016, Lyon
URL: http://www.theses.fr/2016LYSE1174
► Le cancer de la prostate est le plus courant en France et la 4ième cause de mortalité par cancer. Les méthodes diagnostics de références actuel…
(more)
▼ Le cancer de la prostate est le plus courant en France et la 4ième cause de mortalité par cancer. Les méthodes diagnostics de références actuel sont souvent insuffisantes pour détecter et localiser précisément une lésion. L’imagerie IRM multi-paramétrique est désormais la technique la plusprometteuse pour le diagnostic et la prise en charge du cancer de la prostate. Néanmoins, l’interprétation visuelle des multiples séquences IRM n’est pas aisée. Dans ces conditions, un fort intérêt s’est porté sur les systèmes d’aide au diagnostic dont le but est d’assister le radiologue dans ses décisions. Cette thèse présente la conception d’un système d’aide à la détection (CADe) dontl’approche finale est de fournir au radiologue une carte de probabilité du cancer dans la zone périphérique de la prostate. Ce CADe repose sur une base d’images IRM multi-paramétrique (IRM-mp) 1.5T de types T2w, dynamique et de diffusion provenant d’une base de 49 patients annotés permettant d’obtenir une vérité terrain par analyse stricte des coupes histologiques des pièces de prostate. Cette thèse met l’accent sur la détection des cancers mais aussisur leur caractérisation dans le but de fournir une carte de probabilité corrélée au grade de Gleason des tumeurs. Nous avons utilisé une méthode d’apprentissage de dictionnaires permettant d’extraire de nouvelles caractéristiques descriptives dont l’objectif est de discriminer chacun des cancers. Ces dernières sont ensuite utilisées par deux classifieurs : régression logistique et séparateur à vaste marge (SVM), permettant de produire une carte de probabilité du cancer. Nous avons concentré nos efforts sur la discrimination des cancers agressifs (Gleason>6) et fourni une analyse de la corrélationentre probabilités et scores de Gleason. Les résultats montrent de très bonnes performances de détection des cancers agressifs et l’analyse des probabilités conclue sur une forte capacité du système à séparer les cancers agressifs du reste des tissus mais ne permet pas aisément de distinguer chacundes grades de cancer
Prostate cancer is the most frequent and the fourth leading cause of mortality in France. Actual diagnosis methods are often insufficient in order to detect and precisely locate cancer. Multiparametrics MRI is now one of the most promising method for accurate follow-up of the disease. However, the visual interpretation of MRI is not easy and it is shown that there is strongvariability among expert radiologists to perform diagnosis, especially when MR sequences are contradictory. Under these circumstances, a strong interest is for Computer-aided diagnosis systems (CAD) aiming at assisting expert radiologist in their final decision. This thesis presents our work toward the conception of a CADe which final goal is to provide a cancer probability map to expertradiologist. This study is based on a rich dataset of 49 patients made of T2w, dynamic and diffusion MR images. The ground truth was obtained through strict process of annotations and correlation between histology and MRI. This thesis focuses…
Advisors/Committee Members: Rouviere, Olivier (thesis director), Lartizien, Carole (thesis director).
Subjects/Keywords: IRM 1.5T; CAD; Classification supervisée; IRM 1.5T; CAD; Supervised classification; 616.075
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APA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lehaire, J. (2016). Détection et caractérisation du cancer de la prostate par images IRM 1.5T multiparamétriques : Computer-aided decision system for prostate cancer detection and characterization based on multi-parametric 1.5T MRI. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2016LYSE1174
Chicago Manual of Style (16th Edition):
Lehaire, Jérôme. “Détection et caractérisation du cancer de la prostate par images IRM 1.5T multiparamétriques : Computer-aided decision system for prostate cancer detection and characterization based on multi-parametric 1.5T MRI.” 2016. Doctoral Dissertation, Lyon. Accessed January 23, 2021.
http://www.theses.fr/2016LYSE1174.
MLA Handbook (7th Edition):
Lehaire, Jérôme. “Détection et caractérisation du cancer de la prostate par images IRM 1.5T multiparamétriques : Computer-aided decision system for prostate cancer detection and characterization based on multi-parametric 1.5T MRI.” 2016. Web. 23 Jan 2021.
Vancouver:
Lehaire J. Détection et caractérisation du cancer de la prostate par images IRM 1.5T multiparamétriques : Computer-aided decision system for prostate cancer detection and characterization based on multi-parametric 1.5T MRI. [Internet] [Doctoral dissertation]. Lyon; 2016. [cited 2021 Jan 23].
Available from: http://www.theses.fr/2016LYSE1174.
Council of Science Editors:
Lehaire J. Détection et caractérisation du cancer de la prostate par images IRM 1.5T multiparamétriques : Computer-aided decision system for prostate cancer detection and characterization based on multi-parametric 1.5T MRI. [Doctoral Dissertation]. Lyon; 2016. Available from: http://www.theses.fr/2016LYSE1174
9.
Walker, Briana Shanise.
Rethinking Document Classification: A Pilot for the
Application of Text Mining Techniques To Enhance Standardized
Assessment Protocols for Critical Care Medical Team Transfer of
Care.
Degree: MSs, Systems Biology and Bioinformatics, 2017, Case Western Reserve University School of Graduate Studies
URL: http://rave.ohiolink.edu/etdc/view?acc_num=case1496760037827537
► The research efforts undertaken in this thesis project represent an extension of the previously published works of Alfes & Reimer (2016). This pilot study evaluates…
(more)
▼ The research efforts undertaken in this thesis project
represent an extension of the previously published works of Alfes
& Reimer (2016). This pilot study evaluates the feasibility of
applying
supervised text
classification to properly label
successful patient handoffs. Using an expertly-created evaluation
rubric as the gold standard for labeling, a variety of document
classification techniques were applied to the transcribed dialogue
of the Lead Flight specialist in LifeFlight simulation exercises.
The purpose of the present work was to establish the effectiveness
of the selected
classification methods as part of natural language
processing algorithm development to automatically identifying
handoffs as successful/unsuccessful. Several different common
preprocessing filtering methods and text classifiers were selected
from the literature and are investigated. The results of the
current research indicate that lowercasing tokens, TF-IDF
transformation, and normalization of document length can have
positive effects on the resulting F1 evaluation metric. Results
from the
classification test runs indicate that the best performing
classifier varies with increasing n-fold cross validation, with
Decision Trees (2-,3-fold) and Multinomial Naive Bayes (5-fold)
yielding top F1-measures.
Advisors/Committee Members: Drummond, Colin (Advisor).
Subjects/Keywords: Bioinformatics; Nursing; critical care; transfer of care; text classification; document classification; supervised classification; dialogue
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APA (6th Edition):
Walker, B. S. (2017). Rethinking Document Classification: A Pilot for the
Application of Text Mining Techniques To Enhance Standardized
Assessment Protocols for Critical Care Medical Team Transfer of
Care. (Masters Thesis). Case Western Reserve University School of Graduate Studies. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1496760037827537
Chicago Manual of Style (16th Edition):
Walker, Briana Shanise. “Rethinking Document Classification: A Pilot for the
Application of Text Mining Techniques To Enhance Standardized
Assessment Protocols for Critical Care Medical Team Transfer of
Care.” 2017. Masters Thesis, Case Western Reserve University School of Graduate Studies. Accessed January 23, 2021.
http://rave.ohiolink.edu/etdc/view?acc_num=case1496760037827537.
MLA Handbook (7th Edition):
Walker, Briana Shanise. “Rethinking Document Classification: A Pilot for the
Application of Text Mining Techniques To Enhance Standardized
Assessment Protocols for Critical Care Medical Team Transfer of
Care.” 2017. Web. 23 Jan 2021.
Vancouver:
Walker BS. Rethinking Document Classification: A Pilot for the
Application of Text Mining Techniques To Enhance Standardized
Assessment Protocols for Critical Care Medical Team Transfer of
Care. [Internet] [Masters thesis]. Case Western Reserve University School of Graduate Studies; 2017. [cited 2021 Jan 23].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1496760037827537.
Council of Science Editors:
Walker BS. Rethinking Document Classification: A Pilot for the
Application of Text Mining Techniques To Enhance Standardized
Assessment Protocols for Critical Care Medical Team Transfer of
Care. [Masters Thesis]. Case Western Reserve University School of Graduate Studies; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1496760037827537
Rochester Institute of Technology
10.
Syed, Abdul Haleem.
Segmentation and Classification of Remotely Sensed Images: Object-Based Image Analysis.
Degree: PhD, Chester F. Carlson Center for Imaging Science (COS), 2015, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/8823
► Land-use-and-land-cover (LULC) mapping is crucial in precision agriculture, environmental monitoring, disaster response, and military applications. The demand for improved and more accurate LULC maps…
(more)
▼ Land-use-and-land-cover (LULC) mapping is crucial in precision agriculture, environmental monitoring, disaster response, and military applications. The demand for improved and more accurate LULC maps has led to the emergence of a key methodology known as Geographic Object-Based Image Analysis (GEOBIA). The core idea of the GEOBIA for an object-based
classification system (OBC) is to change the unit of analysis from single-pixels to groups-of-pixels called `objects' through segmentation. While this new paradigm solved problems and improved global accuracy, it also raised new challenges such as the loss of accuracy in categories that are less abundant, but potentially important. Although this trade-off may be acceptable in some domains, the consequences of such an accuracy loss could be potentially fatal in others (for instance, landmine detection).
This thesis proposes a method to improve OBC performance by eliminating such accuracy losses. Specifically, we examine the two key players of an OBC system : Hierarchical Segmentation and
Supervised Classification. Further, we propose a model to understand the source of accuracy errors in minority categories and provide a method called Scale Fusion to eliminate those errors. This proposed fusion method involves two stages. First, the characteristic scale for each category is estimated through a combination of segmentation and
supervised classification. Next, these estimated scales (segmentation maps) are fused into one combined-object-map.
Classification performance is evaluated by comparing results of the multi-cut-and-fuse approach (proposed) to the traditional single-cut (SC) scale selection strategy. Testing on four different data sets revealed that our proposed algorithm improves accuracy on minority classes while performing just as well on abundant categories.
Another active obstacle, presented by today's remotely sensed images, is the volume of information produced by our modern sensors with high spatial and temporal resolution. For instance, over this decade, it is projected that 353 earth observation satellites from 41 countries are to be launched. Timely production of geo-spatial information, from these large volumes, is a challenge. This is because in the traditional methods, the underlying representation and information processing is still primarily pixel-based, which implies that as the number of pixels increases, so does the computational complexity. To overcome this bottleneck, created by pixel-based representation, this thesis proposes a dart-based discrete topological representation (DBTR), where the DBTR differs from pixel-based methods in its use of a reduced boundary based representation. Intuitively, the efficiency gains arise from the observation that, it is lighter to represent a region by its boundary (darts) than by its area (pixels). We found that our implementation of DBTR, not only improved our computational efficiency, but also enhanced our ability to encode and extract spatial information.
Overall, this thesis presents…
Advisors/Committee Members: Eli Saber.
Subjects/Keywords: Hierarchical segmentation; Scale selection; Scale space analysis; Supervised classification
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APA (6th Edition):
Syed, A. H. (2015). Segmentation and Classification of Remotely Sensed Images: Object-Based Image Analysis. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8823
Chicago Manual of Style (16th Edition):
Syed, Abdul Haleem. “Segmentation and Classification of Remotely Sensed Images: Object-Based Image Analysis.” 2015. Doctoral Dissertation, Rochester Institute of Technology. Accessed January 23, 2021.
https://scholarworks.rit.edu/theses/8823.
MLA Handbook (7th Edition):
Syed, Abdul Haleem. “Segmentation and Classification of Remotely Sensed Images: Object-Based Image Analysis.” 2015. Web. 23 Jan 2021.
Vancouver:
Syed AH. Segmentation and Classification of Remotely Sensed Images: Object-Based Image Analysis. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2015. [cited 2021 Jan 23].
Available from: https://scholarworks.rit.edu/theses/8823.
Council of Science Editors:
Syed AH. Segmentation and Classification of Remotely Sensed Images: Object-Based Image Analysis. [Doctoral Dissertation]. Rochester Institute of Technology; 2015. Available from: https://scholarworks.rit.edu/theses/8823
Rochester Institute of Technology
11.
Karnam, Srivallabha.
Self-Supervised Learning for Segmentation using Image Reconstruction.
Degree: MS, Computer Engineering, 2020, Rochester Institute of Technology
URL: https://scholarworks.rit.edu/theses/10532
► Deep learning is the engine that is piloting tremendous growth in various segments of the industry by consuming valuable fuel called data. We are…
(more)
▼ Deep learning is the engine that is piloting tremendous growth in various segments of the industry by consuming valuable fuel called data. We are witnessing many businesses adopting this technology be it healthcare, transportation, defense, semiconductor, or retail. But most of the accomplishments that we see now rely on
supervised learning.
Supervised learning needs a substantial volume of labeled data which are usually annotated by humans- an arduous and expensive task often leading to datasets that are insufficient in size or human labeling errors. The performance of deep learning models is only as good as the data. Self-
supervised learning minimizes the need for labeled data as it extracts the pertinent context and inherited data content. We are inspired by image interpolation where we resize an image from a one-pixel grid to another. We introduce a novel self-
supervised learning method specialized for semantic segmentation tasks. We use Image reconstruction as a pre-text task where pixels and or pixel channel (R or G or B pixel channel) in the input images are dropped in a defined or random manner and the original image serves as ground truth. We use the ImageNet dataset for a pretext learning task, and PASCAL V0C to evaluate efficacy of proposed methods. In segmentation tasks decoder is equally important as the encoder, since our proposed method learns both the encoder and decoder as a part of a pretext task, our method outperforms existing self-
supervised segmentation methods.
Advisors/Committee Members: Raymond Ptucha.
Subjects/Keywords: Classification; Computer vision; Self-supervised learning; Semantic segmentation; Unsupervised learning
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APA (6th Edition):
Karnam, S. (2020). Self-Supervised Learning for Segmentation using Image Reconstruction. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10532
Chicago Manual of Style (16th Edition):
Karnam, Srivallabha. “Self-Supervised Learning for Segmentation using Image Reconstruction.” 2020. Masters Thesis, Rochester Institute of Technology. Accessed January 23, 2021.
https://scholarworks.rit.edu/theses/10532.
MLA Handbook (7th Edition):
Karnam, Srivallabha. “Self-Supervised Learning for Segmentation using Image Reconstruction.” 2020. Web. 23 Jan 2021.
Vancouver:
Karnam S. Self-Supervised Learning for Segmentation using Image Reconstruction. [Internet] [Masters thesis]. Rochester Institute of Technology; 2020. [cited 2021 Jan 23].
Available from: https://scholarworks.rit.edu/theses/10532.
Council of Science Editors:
Karnam S. Self-Supervised Learning for Segmentation using Image Reconstruction. [Masters Thesis]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10532
University of Tasmania
12.
Anees, A.
Statistical algorithms for land/forest cover change detection using remote sensing data.
Degree: 2016, University of Tasmania
URL: https://eprints.utas.edu.au/22984/1/Anees_whole_thesis.pdf
► Land cover changes significantly affect climate, hydrology, bio-diversity, socio-economic stability and food security. Some of these changes being studied in remote sensing discipline include, but…
(more)
▼ Land cover changes significantly affect climate, hydrology, bio-diversity, socio-economic
stability and food security. Some of these changes being studied in remote sensing discipline
include, but are not limited to, anthropogenic changes e.g. clear-cutting of forests
for human settlements, and beetle/insect infestations in the forests. Beetle/insect infestations
cause considerable damage to the forests resulting in tree mortality on large scale
which provides fuel for fires and wastes valuable wood. Therefore, early detection of such
changes is often desired by the authorities in order to carry out timely actions to mitigate
them. However, manual monitoring using high resolution photography or field surveys
can become very difficult and time consuming or even infeasible because such changes
cover very large areas. This necessitates development of automated remote sensing algorithms
which can monitor large areas with minimal human intervention. Several land
cover change detection algorithms exist in literature which utilize remotely sensed imagery
captured by different satellites. However, there are a very few studies which detect
such changes in near-real time manner. Furthermore, there is still a room for improvement
in the detection accuracy, detection delays and computational complexity of such algorithms.
This thesis utilizes coarse (500 m) and moderate (30 m) spatial resolution satellite
imagery (MODIS and Landsat 7 ETM+, respectively) and proposes four statistical algorithms
for detection of land cover changes with significant improvements. The first
algorithm (published in IEEE Journal of Selected Topics in Applied Earth Observations
and Remote Sensing) is a supervised technique developed for near-real time detection of
beetle infestations in pine forests of North America (British Columbia and Colorado). It
models the hyper-temporal multi-spectral MODIS Vegetation Index (VI) time series with
a triply modulated cosine function using a sliding window non-linear least squares and
applies a change metric based on log-likelihood ratios to the trend parameter time-series
of the fitted model, instead of the raw vegetation index. Significant improvement, in the detection accuracy with reduced detection delays, was achieved with this first published
algorithm. The second algorithm (published in IEEE Geoscience and Remote Sensing
Letters) is unsupervised and makes use of properties of Martingale Central Limit Theorem
(MCLT) in the change metric derived from the parameter time series, in order to
avoid threshold tuning while detecting beetle infestation in MODIS vegetation index time
series. The third algorithm (published in IEEE Journal of Selected Topics in Applied
Earth Observations and Remote Sensing) avoids the Gaussian distribution based change
metrics, one of the limitations of the existing methods, and improves the change detection
accuracy and detection delays significantly by using assumption free, directly estimated
relative density-ratio based Repeated Sequential Probability…
Subjects/Keywords: Change detection; remote sensing; statistical algorithms; supervised classification
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Anees, A. (2016). Statistical algorithms for land/forest cover change detection using remote sensing data. (Thesis). University of Tasmania. Retrieved from https://eprints.utas.edu.au/22984/1/Anees_whole_thesis.pdf
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Anees, A. “Statistical algorithms for land/forest cover change detection using remote sensing data.” 2016. Thesis, University of Tasmania. Accessed January 23, 2021.
https://eprints.utas.edu.au/22984/1/Anees_whole_thesis.pdf.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Anees, A. “Statistical algorithms for land/forest cover change detection using remote sensing data.” 2016. Web. 23 Jan 2021.
Vancouver:
Anees A. Statistical algorithms for land/forest cover change detection using remote sensing data. [Internet] [Thesis]. University of Tasmania; 2016. [cited 2021 Jan 23].
Available from: https://eprints.utas.edu.au/22984/1/Anees_whole_thesis.pdf.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Anees A. Statistical algorithms for land/forest cover change detection using remote sensing data. [Thesis]. University of Tasmania; 2016. Available from: https://eprints.utas.edu.au/22984/1/Anees_whole_thesis.pdf
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
McMaster University
13.
Gallaugher, Michael P.B.
On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution.
Degree: MSc, 2016, McMaster University
URL: http://hdl.handle.net/11375/20738
► Recent work on fractionally-supervised classification (FSC), an approach that allows classification to be carried out with a fractional amount of weight given to the unla-…
(more)
▼ Recent work on fractionally-supervised classification (FSC), an approach that allows classification to be carried out with a fractional amount of weight given to the unla- belled points, is extended in two important ways. First, and of fundamental impor- tance, the question over how to choose the amount of weight given to the unlabelled points is addressed. Then, the FSC approach is extended to mixtures of multivariate t-distributions. The first extension is essential because it makes FSC more readily applicable to real problems. The second, although less fundamental, demonstrates the efficacy of FSC beyond Gaussian mixture models.
Thesis
Master of Science (MSc)
Advisors/Committee Members: McNicholas, Paul D., Mathematics and Statistics.
Subjects/Keywords: Fractionally Supervised Classification; Clustering; Discriminant Analysis; Mixture Models
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APA (6th Edition):
Gallaugher, M. P. B. (2016). On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/20738
Chicago Manual of Style (16th Edition):
Gallaugher, Michael P B. “On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution.” 2016. Masters Thesis, McMaster University. Accessed January 23, 2021.
http://hdl.handle.net/11375/20738.
MLA Handbook (7th Edition):
Gallaugher, Michael P B. “On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution.” 2016. Web. 23 Jan 2021.
Vancouver:
Gallaugher MPB. On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution. [Internet] [Masters thesis]. McMaster University; 2016. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/11375/20738.
Council of Science Editors:
Gallaugher MPB. On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution. [Masters Thesis]. McMaster University; 2016. Available from: http://hdl.handle.net/11375/20738
Tampere University
14.
Hussain, Yasir.
Predicting customer satisfaction with product reviews: A comparitive study of some machine learning approaches.
Degree: 2019, Tampere University
URL: https://trepo.tuni.fi/handle/10024/118686
► In past two decades e-commerce platform developed exponentially, and with this advent, there came several challenges due to a vast amount of information. Customers not…
(more)
▼ In past two decades e-commerce platform developed exponentially, and with this advent, there came several challenges due to a vast amount of information. Customers not only buy products online but also get valuable information about a product they intend to buy through an online platform. Customers share their experiences by providing feedback which creates a pool of textual information and this process continuously generates data every day. The information provided by customers contains both subjective and objective text that contains a rich information regarding behaviour, liking and disliking towards a product and sentiments of customers. Moreover, this information can be helpful for the customers who are yet to buy or who are yet in decision making process. This thesis studies comparison of four supervised machine learning approaches to predict customer satisfaction. These approaches are: Naïve Bayes, Support Vector Machines (SVM), Logistic Regression (LR), and Decision Tree (DT). The models use term frequency inverse document frequency (TF-IDF) vectorization for training and testing sets of data. The models are applied after basic pre-processing of text data that includes the lower casing, lemmatization, the stop words removal, smileys removal, and digits removal. We compare the performance of models using accuracy, precision, recall, and F1-scores. Support Vector Machines (SVM) outperforms the rest of the models with the accuracy rate 83% while Naïve Bayes, Logistic Regression (LR) and Decision Tree (DT) have accuracy rate 82%, 78%, and 76%, respectively. Moreover, we evaluate the performance of classifiers using confusion matrix.
Subjects/Keywords: Supervised Machine Learning; NLP; Amazon Reviews; Customer Satisfaction; Classification
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Manager
APA (6th Edition):
Hussain, Y. (2019). Predicting customer satisfaction with product reviews: A comparitive study of some machine learning approaches.
(Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/118686
Chicago Manual of Style (16th Edition):
Hussain, Yasir. “Predicting customer satisfaction with product reviews: A comparitive study of some machine learning approaches.
” 2019. Masters Thesis, Tampere University. Accessed January 23, 2021.
https://trepo.tuni.fi/handle/10024/118686.
MLA Handbook (7th Edition):
Hussain, Yasir. “Predicting customer satisfaction with product reviews: A comparitive study of some machine learning approaches.
” 2019. Web. 23 Jan 2021.
Vancouver:
Hussain Y. Predicting customer satisfaction with product reviews: A comparitive study of some machine learning approaches.
[Internet] [Masters thesis]. Tampere University; 2019. [cited 2021 Jan 23].
Available from: https://trepo.tuni.fi/handle/10024/118686.
Council of Science Editors:
Hussain Y. Predicting customer satisfaction with product reviews: A comparitive study of some machine learning approaches.
[Masters Thesis]. Tampere University; 2019. Available from: https://trepo.tuni.fi/handle/10024/118686
Universidade Nova
15.
Last, Felix.
Oversampling for imbalanced learning based on k-means and smote.
Degree: 2018, Universidade Nova
URL: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/31042
► Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class…
(more)
▼ Learning from class-imbalanced data continues to be a common and challenging problem in
supervised learning as standard
classification algorithms are designed to handle balanced class
distributions. While different strategies exist to tackle this problem, methods which generate
artificial data to achieve a balanced class distribution are more versatile than modifications to the
classification algorithm. Such techniques, called oversamplers, modify the training data, allowing any
classifier to be used with class-imbalanced datasets. Many algorithms have been proposed for this
task, but most are complex and tend to generate unnecessary noise. This work presents a simple and
effective oversampling method based on k-means clustering and SMOTE oversampling, which avoids
the generation of noise and effectively overcomes imbalances between and within classes. Empirical
results of extensive experiments with 71 datasets show that training data oversampled with the
proposed method improves
classification results. Moreover, k-means SMOTE consistently
outperforms other popular oversampling methods. An implementation is made available in the
python programming language.
Advisors/Committee Members: Bação, Fernando José Ferreira Lucas.
Subjects/Keywords: Class-imbalanced learning; Oversampling; Classification; Clustering; Supervised learning
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Manager
APA (6th Edition):
Last, F. (2018). Oversampling for imbalanced learning based on k-means and smote. (Thesis). Universidade Nova. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/31042
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Last, Felix. “Oversampling for imbalanced learning based on k-means and smote.” 2018. Thesis, Universidade Nova. Accessed January 23, 2021.
https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/31042.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Last, Felix. “Oversampling for imbalanced learning based on k-means and smote.” 2018. Web. 23 Jan 2021.
Vancouver:
Last F. Oversampling for imbalanced learning based on k-means and smote. [Internet] [Thesis]. Universidade Nova; 2018. [cited 2021 Jan 23].
Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/31042.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Last F. Oversampling for imbalanced learning based on k-means and smote. [Thesis]. Universidade Nova; 2018. Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/31042
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Illinois – Chicago
16.
Randazzo, Ettore.
Inferring Interaction Network from Sensor Data.
Degree: 2016, University of Illinois – Chicago
URL: http://hdl.handle.net/10027/21233
► Being able to observe how animals interact among themselves has always been a crucial requirement for behavioral scientists who study social species. Physically watching them…
(more)
▼ Being able to observe how animals interact among themselves has always been a crucial requirement for behavioral scientists who study social species. Physically watching them to see when interactions occur is extremely time-consuming, results in many missed observations and it becomes extremely difficult to do for a very extended period of time. This is especially true for animals who live in large territories and for animals who behave differently when nearby human beings. To overcome these problems, scientists are accustomed to use several different kinds of sensors which are usually attached to the target animals to record some kind of raw data which, once collected, is used to analyze the behavior of their hosts.
The sensors have to be as least invasive as possible for the animal to behave as if it wasn’t wearing them. This implies that sensors have to be light with respect to the animal, they must not emit a considerable amount of heat and the wavelength they use must not be perceptible by the animals near them.
The most popular type of data extracted from sensors attached to animals is Global Posi- tioning System (GPS) data. GPS data is very easy to extract and it can be used to efficiently track the positions of entire groups of animals. However, when we are interested in pairwise interactions between animals and not in their positions, GPS data is not very reliable either because of its low accuracy and because it might not be in line of sight with the satellites it relies on.
In this study, we introduce synthesized sensor data based on a type of non-invasive short range proximity sensor in order to understand whether some animals interact among themselves at a given time. It is essential to label the data in order to be aware of the interactions as they occur during the traning phase.
The sensors whose data we are interested in synthesizing can be used on animals that are capable of wearing them due to weight or size constraints and as long as the sensor range contains the interaction range of the animals we are interested in. The models we analyze in this work don’t assume either that all of the animals behave uniformly amongst each other and also the independence amongst interactions.
We present a framework to synthesize proximity, location and speed data extracted from sensors with several different configurations and we present models to infer animal interactions. Finally, we evaluate the methodology we use by proposing a case study where we perform different analyses to understand when our models are fit for the inference task, what are the most critical parameters impacting their performances and when we should start assuming
independence conditions to simplify the task.
Advisors/Committee Members: Berger-Wolf, Tanya (advisor), DasGupta, Bhaskar (committee member), Lanzi, Pier Luca (committee member).
Subjects/Keywords: animal behaviour; supervised learning; inference; classification; machine learning; dyadic interactions
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Randazzo, E. (2016). Inferring Interaction Network from Sensor Data. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21233
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Randazzo, Ettore. “Inferring Interaction Network from Sensor Data.” 2016. Thesis, University of Illinois – Chicago. Accessed January 23, 2021.
http://hdl.handle.net/10027/21233.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Randazzo, Ettore. “Inferring Interaction Network from Sensor Data.” 2016. Web. 23 Jan 2021.
Vancouver:
Randazzo E. Inferring Interaction Network from Sensor Data. [Internet] [Thesis]. University of Illinois – Chicago; 2016. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/10027/21233.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Randazzo E. Inferring Interaction Network from Sensor Data. [Thesis]. University of Illinois – Chicago; 2016. Available from: http://hdl.handle.net/10027/21233
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
KTH
17.
Lundberg, Ludvig.
Damage Assessment of Mozambique Flooding Using Sentinel.
Degree: Geoinformatics, 2020, KTH
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272396
► In the past 40 years, floods have become a bane of Mozambique’s inhabitants and economy. The latest of them, caused by the cyclone Idai,…
(more)
▼ In the past 40 years, floods have become a bane of Mozambique’s inhabitants and economy. The latest of them, caused by the cyclone Idai, has devastated the area resulting in loss of life and property. It was estimated that around 715 000 hectares of farmland was destroyed as a result of the cyclone. The main goal of this thesis was to assess the extent of the flooding and to determine the types of land cover that were affected. This was done in Google Earth Engine, using SAR change detection on Sentinel 1 data to create a mask for the flooded areas, followed by a supervised image classification on Sentinel 2 data to identify the types of land cover that were flooded. Two classifications were done, using imagery from early periods of the country’s plant growing season and later periods of the same season, respectively. The results of both classifications were below standard, with the main problems stemming from difficulties with differentiating between agriculture and roads along with agriculture and vegetation. Multiple ways to improve the results and avoid the errors in future similar projects were discussed, including using multi temporal data and utilizing a road map for the area to create a large amount of training points for the classification. In conclusion, while the results were not as good as was envisioned, the thesis provided ample opportunity to analyze errors and to theorize methods for improving future work.
Subjects/Keywords: change detection; supervised classification; sentinel; Mozambique; Other Social Sciences; Annan samhällsvetenskap
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APA (6th Edition):
Lundberg, L. (2020). Damage Assessment of Mozambique Flooding Using Sentinel. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272396
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Lundberg, Ludvig. “Damage Assessment of Mozambique Flooding Using Sentinel.” 2020. Thesis, KTH. Accessed January 23, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272396.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lundberg, Ludvig. “Damage Assessment of Mozambique Flooding Using Sentinel.” 2020. Web. 23 Jan 2021.
Vancouver:
Lundberg L. Damage Assessment of Mozambique Flooding Using Sentinel. [Internet] [Thesis]. KTH; 2020. [cited 2021 Jan 23].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272396.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lundberg L. Damage Assessment of Mozambique Flooding Using Sentinel. [Thesis]. KTH; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272396
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Miami
18.
Quirino, Thiago S.
Improving Search In Genetic Algorithms Through Instinct-Based Mating Strategies.
Degree: PhD, Electrical and Computer Engineering (Engineering), 2012, University of Miami
URL: https://scholarlyrepository.miami.edu/oa_dissertations/737
► The Genetic Algorithm (GA) is a popular approach to search and optimization that has been applied to hundreds of real-world optimization problems across numerous domains…
(more)
▼ The Genetic Algorithm (GA) is a popular approach to search and optimization that has been applied to hundreds of real-world optimization problems across numerous domains of science. The GA describes an iterative search process that seeks to improve the quality of an initially random set of solutions with respect to some user-defined optimization criteria. The components of this iterative search process mimic Darwinian biological evolutionary processes such as mating, recombination, mutation, and survival of the fittest. Over the years, researchers have attempted to improve various components of the GA search process. However, the impact of the mating strategy, which determines how existing solutions to a problem are paired during the genetic search process to generate new and better solutions, has so far been neglected in the rich and vast GA literature. In this work, five novel mating strategies inspired from the Darwinian evolutionary principle of "opposites-attract" are proposed to speed up the GA search process. The impact of the proposed mating strategies on the GA’s performance is tested on two well-established and complex testbed optimization problems from the domain of
supervised classification: 1) the 1-NN Tuning problems, and 2) the Optimal Decision Forests problem. The results from rigorous experiments with various UCI data sets reveal that the proposed mating strategies both accelerate the GA search and lead to the discovery of better solutions. Moreover, these improvements come at the cost of only negligible additional computational overhead.
Advisors/Committee Members: Miroslav Kubat, Kamal Premaratne, Michael Scordilis, Nigel M. John, Sundararaman G. Gopalakrishnan.
Subjects/Keywords: Genetic Algorithms; Mating Strategies; Machine Learning; Optimization; Data Mining; Supervised Classification
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APA (6th Edition):
Quirino, T. S. (2012). Improving Search In Genetic Algorithms Through Instinct-Based Mating Strategies. (Doctoral Dissertation). University of Miami. Retrieved from https://scholarlyrepository.miami.edu/oa_dissertations/737
Chicago Manual of Style (16th Edition):
Quirino, Thiago S. “Improving Search In Genetic Algorithms Through Instinct-Based Mating Strategies.” 2012. Doctoral Dissertation, University of Miami. Accessed January 23, 2021.
https://scholarlyrepository.miami.edu/oa_dissertations/737.
MLA Handbook (7th Edition):
Quirino, Thiago S. “Improving Search In Genetic Algorithms Through Instinct-Based Mating Strategies.” 2012. Web. 23 Jan 2021.
Vancouver:
Quirino TS. Improving Search In Genetic Algorithms Through Instinct-Based Mating Strategies. [Internet] [Doctoral dissertation]. University of Miami; 2012. [cited 2021 Jan 23].
Available from: https://scholarlyrepository.miami.edu/oa_dissertations/737.
Council of Science Editors:
Quirino TS. Improving Search In Genetic Algorithms Through Instinct-Based Mating Strategies. [Doctoral Dissertation]. University of Miami; 2012. Available from: https://scholarlyrepository.miami.edu/oa_dissertations/737
University of Houston
19.
Amalaman, Paul K. 1966-.
New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications.
Degree: PhD, Computer Science, 2015, University of Houston
URL: http://hdl.handle.net/10657/4888
► Obtaining hierarchical organizations of knowledge is important in many domains. To create such hierarchies, improved techniques for subdividing entities hierarchically ac-cording to similarities and differences…
(more)
▼ Obtaining hierarchical organizations of knowledge is important in many domains. To create such hierarchies, improved techniques for subdividing entities hierarchically ac-cording to similarities and differences are needed. New techniques for organizing docu-ments in hierarchies, for automatic document retrieval and for hierarchical query cluster-ing are being made available at a fast pace. In this work, we investigate new methods to induce hierarchical models with the goal of obtaining better predictive models, to facili-tate the creation of background knowledge with respect to an underlining class distribu-tion, to obtain hierarchical groupings of a set of objects based on background knowledge they share, to detect sub-classes within existing class distribution, and to provide methods to evaluate hierarchical groupings. The results of this effort has led to the development of (1) TPRTI, a new regression tree induction approach which uses turning points, candi-dates split points computed before the recursive process takes place, to recursively split the node datasets; (2) PATHFINDER, a new
classification tree induction capable of in-ducing very short trees with high accuracies for the price of not classifying examples deemed difficult to classify; (3) AVALANCHE, a new hierarchical divisive clustering approach which takes as input a distance matrix and forms clusters maximizing inter-cluster distances; (4) STAXAC, a new agglomerative clustering approach which creates
supervised taxonomies that unlike traditional agglomerative clustering, which only uses proximity as the single criterion for merging, uses both proximity and class labels infor-mation to obtain hierarchical groupings of a set of objects. We applied the techniques we developed, (1) to molecular phylogenetic-based taxonomy generation and found that this new approach and the obtained
supervised taxonomies can help biologists better charac-terize organisms according to some characteristics of interest such as diseases, growth rate, etc.; (2) to data editing; we were able to enhance the accuracy of the k-nearest neighbor classifier by removing minority class examples from clusters that were extracted from a
supervised taxonomy; (3) to meta learning; we developed new algorithms that operate on
supervised taxonomies and compute both the distribution of the classes within a dataset, and the difficulty of classifying examples belonging to a particular dataset.
Advisors/Committee Members: Eick, Christoph F. (advisor), Vilalta, Ricardo (committee member), Shi, Weidong (committee member), Shah, Shishir Kirit (committee member), Cooper, Timothy F. (committee member).
Subjects/Keywords: Decision trees; Regression tree; Classification tree; Supervised taxonomy; Hierarchical clustering
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APA (6th Edition):
Amalaman, P. K. 1. (2015). New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/4888
Chicago Manual of Style (16th Edition):
Amalaman, Paul K 1966-. “New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications.” 2015. Doctoral Dissertation, University of Houston. Accessed January 23, 2021.
http://hdl.handle.net/10657/4888.
MLA Handbook (7th Edition):
Amalaman, Paul K 1966-. “New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications.” 2015. Web. 23 Jan 2021.
Vancouver:
Amalaman PK1. New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications. [Internet] [Doctoral dissertation]. University of Houston; 2015. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/10657/4888.
Council of Science Editors:
Amalaman PK1. New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications. [Doctoral Dissertation]. University of Houston; 2015. Available from: http://hdl.handle.net/10657/4888
University of Connecticut
20.
zhao, xiaojun.
Machine Learning Approaches to 3D Model Classification.
Degree: MS, Mechanical Engineering, 2015, University of Connecticut
URL: https://opencommons.uconn.edu/gs_theses/820
► A desirable 3D model classification system should be equipped with qualities such as highly correct classification accuracy, good enough classification speed, robustness to model…
(more)
▼ A desirable 3D model
classification system should be equipped with qualities such as highly correct
classification accuracy, good enough
classification speed, robustness to model noises and etc. Our objectives of this thesis are to 1)analyze different shape descriptors to concisely capture the geometric information of a shape in a finite feature descriptor 2) and train multiple multi-class
supervised machine learning classifiers and evaluate their effectiveness in classifying 3D shapes. At first, some of the most representative shape descriptors of different categories are discussed. Then, we also review some mathematical background of
supervised machine learning algorithms that will be implemented. We conduct our experimentations on our own created 3D database, in which all models are downloaded from Google 3D warehouse. We compare
classification performances by applying different machine learning algorithms combined with three shape feature extraction methodologies: Light-field descriptor(LFD)+Angular radial transform descriptors(ARTD),Light-field descriptors (LFD)+ 2D Zernike descriptors(2D ZD), 3D Zernike descriptors(3D ZD). Our experimental results show that above three presented shape descriptors are effective in classifying 3D models. Also, we also extend the
classification of virtual models to the real world point cloud models and evaluate its performance. Conclusions and possible future work directions are presented at the end of this thesis.
Advisors/Committee Members: George Lykotrafitis, Xu Chen, Horea Ilies.
Subjects/Keywords: 3D model; Classification; Machine learning; Shape Descriptors; Supervised Learning
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APA (6th Edition):
zhao, x. (2015). Machine Learning Approaches to 3D Model Classification. (Masters Thesis). University of Connecticut. Retrieved from https://opencommons.uconn.edu/gs_theses/820
Chicago Manual of Style (16th Edition):
zhao, xiaojun. “Machine Learning Approaches to 3D Model Classification.” 2015. Masters Thesis, University of Connecticut. Accessed January 23, 2021.
https://opencommons.uconn.edu/gs_theses/820.
MLA Handbook (7th Edition):
zhao, xiaojun. “Machine Learning Approaches to 3D Model Classification.” 2015. Web. 23 Jan 2021.
Vancouver:
zhao x. Machine Learning Approaches to 3D Model Classification. [Internet] [Masters thesis]. University of Connecticut; 2015. [cited 2021 Jan 23].
Available from: https://opencommons.uconn.edu/gs_theses/820.
Council of Science Editors:
zhao x. Machine Learning Approaches to 3D Model Classification. [Masters Thesis]. University of Connecticut; 2015. Available from: https://opencommons.uconn.edu/gs_theses/820
Université Catholique de Louvain
21.
Amouh, Teh.
Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile.
Degree: 2011, Université Catholique de Louvain
URL: http://hdl.handle.net/2078.1/105005
► The amount of data in the world and in our lives seems ever-increasing and there is no end in sight. Such a situation is supported…
(more)
▼ The amount of data in the world and in our lives seems ever-increasing and there is no end in sight. Such a situation is supported by omnipresent computers along with inexpensive disks and online storage. In order to get the best from these data that overwhelm us, computers give us the opportunity to analyse them for decision making. For example, as reported in the literature, dairy farmers in New Zealand have to make a tough business decision every year : which cows to retain in their herd and which to sell off to an abattoir. Each cow's breeding and milk production history, age, health problems, and and many other factors influence this decision. About 700 attributes for each of several million cows have been recorded over the years. This is an example of large data set (large number of individuals : several millions) containing high-dimensional descriptions (large number of variables : 700). Classically, the observed value on each variable for each individual has a scalar data type.
Large multidimensional data sets abound in real applications. Large size and high dimension are two aspects of the complexity inherent in these data sets. In the framework of this thesis, we are not interested in the complexity induced by the dimensionality...
One way to deal with large size data sets is to summarize the data and use adequate methods for mining the summarized data. Summarising data, as we understand it here, does not reduce the dimensionality. It reduces the number of individuals. In the summarized data table, each row can be viewed as the description of a concept, which is a high level individual (for example a given species of birds) containing lower level individuals in its extent (for instance all birds from the given species). The variability of elements in an extent should be revealed by the row describing the concept in the summarized table, hence the use of data structures in the cells of the summarized data table.
Summarising data using data structures leads to descriptors which are no longer scalar type attributes. For instance, knowing that a cow nears the end of its productive life at 8 years, one might group together all cows which are at least 8 years old. For such a group, the observed value on attribute 'AGE' would be an interval like [8,15], if we assume that there is no cow over 15 years old. Likewise, the other attributes used to describe a group of cows would be structure-valued, leading to a non-standard data table. In other words, each value of a variable used to describe a group of cows would be a structure. Intervals, multivalued-data, distributions, histograms, functions, time series, graphs, and so on are examples of structures.
To our knowledge, there is currently only one complete software environment that is publicly and freely available for tabular structure-valued data analysis. The Symbolic Objects Data Analysis System (SODAS2) was designed and implemented as a "black-box" and consequently does not provide necessary flexibility and adaptability in order to support research activity…
Advisors/Committee Members: UCL - SST/ICTM/ELEN - Pôle en ingénierie électrique, Macq, Benoît, Noirhomme-Fraiture, Monique, De Vleeschouwer, Christophe, Batagelj, Vladimir, Gosselin , Bernard, Lechevallier, Yves, Verleysen, Michel.
Subjects/Keywords: Supervised classification; Clustering; Decision trees; Non-standard data; Sleep analysis
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APA (6th Edition):
Amouh, T. (2011). Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/105005
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Amouh, Teh. “Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile.” 2011. Thesis, Université Catholique de Louvain. Accessed January 23, 2021.
http://hdl.handle.net/2078.1/105005.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Amouh, Teh. “Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile.” 2011. Web. 23 Jan 2021.
Vancouver:
Amouh T. Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile. [Internet] [Thesis]. Université Catholique de Louvain; 2011. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/2078.1/105005.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Amouh T. Analysis of tabular non-standard data with decision trees, and application to hypnogram-based detection of sleep profile. [Thesis]. Université Catholique de Louvain; 2011. Available from: http://hdl.handle.net/2078.1/105005
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
22.
Webb, Dean.
Efficient piecewise linear classifiers and applications.
Degree: PhD, 2011, Federation University Australia
URL: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61668
► Supervised learning has become an essential part of data mining for industry, military, science and academia. Classification, a type of supervised learning allows a machine…
(more)
▼ Supervised learning has become an essential part of data mining for industry, military, science and academia. Classification, a type of supervised learning allows a machine to learn from data to then predict certain behaviours, variables or outcomes. Classification can be used to solve many problems including the detection of malignant cancers, potentially bad creditors and even enabling autonomy in robots. The ability to collect and store large amounts of data has increased significantly over the past few decades. However, the ability of classification techniques to deal with large scale data has not been matched. Many data transformation and reduction schemes have been tried with mixed success. This problem is further exacerbated when dealing with real time classification in embedded systems. The real time classifier must classify using only limited processing, memory and power resources. Piecewise linear boundaries are known to provide efficient real time classifiers. They have low memory requirements, require little processing effort, are parameterless and classify in real time. Piecewise linear functions are used to approximate non-linear decision boundaries between pattern classes. Finding these piecewise linear boundaries is a difficult optimization problem that can require a long training time. Multiple optimization approaches have been used for real time classification, but can lead to suboptimal piecewise linear boundaries. This thesis develops three real time piecewise linear classifiers that deal with large scale data. Each classifier uses a single optimization algorithm in conjunction with an incremental approach that reduces the number of points as the decision boundaries are built. Two of the classifiers further reduce complexity by augmenting the incremental approach with additional schemes. One scheme uses hyperboxes to identify points inside the so-called “indeterminate” regions. The other uses a polyhedral conic set to identify data points lying on or close to the boundary. All other points are excluded from the process of building the decision boundaries. The three classifiers are applied to real time data classification problems and the results of numerical experiments on real world data sets are reported. These results demonstrate that the new classifiers require a reasonable training time and their test set accuracy is consistently good on most data sets compared with current state of the art classifiers.
Doctor of Philosophy
Subjects/Keywords: Data mining; Data classification; Supervised learning; Artificial intelligence; Knowledge-based systems
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APA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Webb, D. (2011). Efficient piecewise linear classifiers and applications. (Doctoral Dissertation). Federation University Australia. Retrieved from http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61668
Chicago Manual of Style (16th Edition):
Webb, Dean. “Efficient piecewise linear classifiers and applications.” 2011. Doctoral Dissertation, Federation University Australia. Accessed January 23, 2021.
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61668.
MLA Handbook (7th Edition):
Webb, Dean. “Efficient piecewise linear classifiers and applications.” 2011. Web. 23 Jan 2021.
Vancouver:
Webb D. Efficient piecewise linear classifiers and applications. [Internet] [Doctoral dissertation]. Federation University Australia; 2011. [cited 2021 Jan 23].
Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61668.
Council of Science Editors:
Webb D. Efficient piecewise linear classifiers and applications. [Doctoral Dissertation]. Federation University Australia; 2011. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61668
Karlstad University
23.
Westlinder, Simon.
Video Traffic Classification : A Machine Learning approach with Packet Based Features using Support Vector Machine.
Degree: Mathematics and Computer Science (from 2013), 2016, Karlstad University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-43011
► Internet traffic classification is an important field which several stakeholders are dependent on for a number of different reasons. Internet Service Providers (ISPs) and…
(more)
▼ Internet traffic classification is an important field which several stakeholders are dependent on for a number of different reasons. Internet Service Providers (ISPs) and network operators benefit from knowing what type of traffic that propagates over their network in order to correctly treat different applications. Today Deep Packet Inspection (DPI) and port based classification are two of the more commonly used methods in order to classify Internet traffic. However, both of these techniques fail when the traffic is encrypted. This study explores a third method, classifying Internet traffic by machine learning in which the classification is realized by looking at Internet traffic flow characteristics instead of actual payloads. Machine learning can solve the inherent limitations that DPI and port based classification suffers from. In this study the Internet traffic is divided into two classes of interest: Video and Other. There exist several machine learning methods for classification, and this study focuses on Support Vector Machine (SVM) to classify traffic. Several traffic characteristics are extracted, such as individual payload sizes and the longest consecutive run of payload packets in the downward direction. Several experiments using different approaches are conducted and the achieved results show that overall accuracies above 90% are achievable.
HITS, 4707
Subjects/Keywords: Supervised Machine Learning; SVM; Video traffic classification; Computer Sciences; Datavetenskap (datalogi)
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APA (6th Edition):
Westlinder, S. (2016). Video Traffic Classification : A Machine Learning approach with Packet Based Features using Support Vector Machine. (Thesis). Karlstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-43011
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Westlinder, Simon. “Video Traffic Classification : A Machine Learning approach with Packet Based Features using Support Vector Machine.” 2016. Thesis, Karlstad University. Accessed January 23, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-43011.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Westlinder, Simon. “Video Traffic Classification : A Machine Learning approach with Packet Based Features using Support Vector Machine.” 2016. Web. 23 Jan 2021.
Vancouver:
Westlinder S. Video Traffic Classification : A Machine Learning approach with Packet Based Features using Support Vector Machine. [Internet] [Thesis]. Karlstad University; 2016. [cited 2021 Jan 23].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-43011.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Westlinder S. Video Traffic Classification : A Machine Learning approach with Packet Based Features using Support Vector Machine. [Thesis]. Karlstad University; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-43011
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
24.
Smalbil, Jos (author).
Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:f5f96ef9-8665-4c93-a932-34b8441976b0
► In order to provide accurate statistics for industries, the classification of enterprises by economic activity is an important task for national statistical institutes. The economic…
(more)
▼ In order to provide accurate statistics for industries, the classification of enterprises by economic activity is an important task for national statistical institutes. The economic activity codes in the Dutch business register are less accurate for small enterprises since small enterprises are not labelled manually. To increase the quality of the register, automatic classification of enterprises based on their websites has been tried with supervised text mining techniques. The performance of current supervised text mining techniques is limited by the available accurately labelled training data. Since inaccurate labels are available for all enterprises, the current study investigates how to leverage the noisy labelled data to improve the economic activity classification of small enterprises based on their webpage texts. The current study compares the performance of various semi-supervised methods that enlarge the training data by leveraging the abundance of noisy labelled data. The methods are compared against a supervised baseline, which uses all noisy data as is. The proposed proportional weakly self-training method queries noisy labelled instances through high probability sampling and filters mispredicted instances. Results showed that proportional weakly self-training improves upon the supervised baseline while requiring far less training instances. From qualitative analyses, we conclude that the filter of proportional weakly self-training reduces error propagation compared to classic self-training. Additional experimental results showed that large enterprises are less suitable as training data for prediction of small enterprises and that top-k performance scores improve results but are not yet sufficient for semi-automatic classification. Further examination of error detection methods is recommended to improve web-based economic activity classification.
Computer Science
Advisors/Committee Members: Lofi, Christoph (mentor), Houben, Geert-Jan (graduation committee), Jongbloed, Geurt (graduation committee), van Delden, A. (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: text mining; label noise; text classification; semi-supervised learning
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APA ·
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Export
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APA (6th Edition):
Smalbil, J. (. (2020). Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f5f96ef9-8665-4c93-a932-34b8441976b0
Chicago Manual of Style (16th Edition):
Smalbil, Jos (author). “Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels.” 2020. Masters Thesis, Delft University of Technology. Accessed January 23, 2021.
http://resolver.tudelft.nl/uuid:f5f96ef9-8665-4c93-a932-34b8441976b0.
MLA Handbook (7th Edition):
Smalbil, Jos (author). “Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels.” 2020. Web. 23 Jan 2021.
Vancouver:
Smalbil J(. Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 23].
Available from: http://resolver.tudelft.nl/uuid:f5f96ef9-8665-4c93-a932-34b8441976b0.
Council of Science Editors:
Smalbil J(. Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:f5f96ef9-8665-4c93-a932-34b8441976b0
University of Minnesota
25.
Valovage, Mark.
Enhancing Machine Learning Classification for Electrical Time Series with Additional Domain Applications.
Degree: PhD, Computer Science, 2019, University of Minnesota
URL: http://hdl.handle.net/11299/211812
► Recent advances in machine learning have significant, far-reaching potential in electrical time series applications. However, many methods cannot currently be implemented in real world applications…
(more)
▼ Recent advances in machine learning have significant, far-reaching potential in electrical time series applications. However, many methods cannot currently be implemented in real world applications due to multiple challenges. This thesis explores solutions to many of these challenges in an effort to realize the full potential of applying machine learning to dynamic electrical systems. This thesis focuses on two areas: electricity disaggregation and time series shapelets. However, the contributions below can be applied to dozens of other domains. Electricity disaggregation identifies individual appliances from one or more aggregate data streams. In first world countries, disaggregation has the potential to eliminate billions of dollars of waste each year, while in developing countries, disaggregation could reduce costs enough to help provide electricity to over a billion people who currently have no access to it. Existing disaggregation methods cannot be applied to real-world households because they are too sensitive to varying noise levels, require parameters to be tuned to individual houses or appliances, make incorrect assumptions about real-world data, or are too resource intensive for inexpensive hardware. This thesis details label correction, a process to automatically correct user-labeled training samples, to increase classification accuracy. It also details an approach to unsupervised learning that is scalable to hundreds of millions of buildings using two novel approaches: event detection without parameter tuning and iterative discovery without appliance models. Time series shapelets are small subsequences of time series used for classification of unlabeled time series. While shapelets can be used for electricity disaggregation, they have applications to dozens of other domains. However, little research has been done on the distance metric used by shapelets. This distance metric is critical, as it is the sole feature a shapelet uses to discriminate between samples from different classes. This thesis details two contributions to time series shapelets. The first, selective z-normalization, is a technique that increases the shapelet classification accuracy by discovering a combination of z-normalized and non-normalized shapelets. The second is computing shapelet-specific distances, a technique to increase accuracy by finding a unique distance metric for each shapelet.
Subjects/Keywords: Classification; Electricity Disaggregation; Shapelets; Supervised Learning; Time Series; Unsupervised Learning
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APA (6th Edition):
Valovage, M. (2019). Enhancing Machine Learning Classification for Electrical Time Series with Additional Domain Applications. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/211812
Chicago Manual of Style (16th Edition):
Valovage, Mark. “Enhancing Machine Learning Classification for Electrical Time Series with Additional Domain Applications.” 2019. Doctoral Dissertation, University of Minnesota. Accessed January 23, 2021.
http://hdl.handle.net/11299/211812.
MLA Handbook (7th Edition):
Valovage, Mark. “Enhancing Machine Learning Classification for Electrical Time Series with Additional Domain Applications.” 2019. Web. 23 Jan 2021.
Vancouver:
Valovage M. Enhancing Machine Learning Classification for Electrical Time Series with Additional Domain Applications. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2021 Jan 23].
Available from: http://hdl.handle.net/11299/211812.
Council of Science Editors:
Valovage M. Enhancing Machine Learning Classification for Electrical Time Series with Additional Domain Applications. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/211812
Georgia State University
26.
Wang, Xiaoyuan.
Data Mining Analysis of the Parkinson's Disease.
Degree: MS, Mathematics and Statistics, 2014, Georgia State University
URL: https://scholarworks.gsu.edu/math_theses/143
► Biological research is becoming increasingly database driven and statistical learning can be used to discover patterns in the biological data. In the thesis, the…
(more)
▼ Biological research is becoming increasingly database driven and statistical learning can be used to discover patterns in the biological data. In the thesis, the
supervised learning approaches are utilized to analyze the Oxford Parkinson’s disease detection data and build models for prediction or
classification. We construct predictive models based on training set, evaluate their performance by applying these models to an independent test set, and find the best methods for predicting whether people have Parkinson’s disease. The proposed artificial neural network procedure outperforms with the best and highest prediction accuracy, while the logistic and probit regressions are preferred statistical models which can offer better interpretation with the higher prediction accuracy compared to other proposed data mining approaches.
Advisors/Committee Members: Yichuan Zhao.
Subjects/Keywords: Supervised Learning; Cross Validation; Prediction Analysis; Classification; Model Selection; ROC Curve
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APA (6th Edition):
Wang, X. (2014). Data Mining Analysis of the Parkinson's Disease. (Thesis). Georgia State University. Retrieved from https://scholarworks.gsu.edu/math_theses/143
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Wang, Xiaoyuan. “Data Mining Analysis of the Parkinson's Disease.” 2014. Thesis, Georgia State University. Accessed January 23, 2021.
https://scholarworks.gsu.edu/math_theses/143.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wang, Xiaoyuan. “Data Mining Analysis of the Parkinson's Disease.” 2014. Web. 23 Jan 2021.
Vancouver:
Wang X. Data Mining Analysis of the Parkinson's Disease. [Internet] [Thesis]. Georgia State University; 2014. [cited 2021 Jan 23].
Available from: https://scholarworks.gsu.edu/math_theses/143.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wang X. Data Mining Analysis of the Parkinson's Disease. [Thesis]. Georgia State University; 2014. Available from: https://scholarworks.gsu.edu/math_theses/143
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Delft University of Technology
27.
Rietveld, T.M. (author).
The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features.
Degree: 2020, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:d9ecc6f2-19ab-4dc4-b3b4-513c7285677d
► Continuous affective self-reports are intrusive and expensive to acquire, forcing researchers to use alternative labels for the construction of their predictive models. The most predominantly…
(more)
▼ Continuous affective self-reports are intrusive and expensive to acquire, forcing researchers to use alternative labels for the construction of their predictive models. The most predominantly used labels in literature are continuous perceived affective labels obtained using external annotators. However an increasing body of research indicates that the relation between expressed emotion and experienced emotion might not be as apparent as previously assumed. Retrospective self-reports provided by participants do capture experienced emotion, but models applied on these labels suffer from the lack of continuous annotations during training. In this work, we aim to answer whether this lack of temporal information can be remedied by using continuous external annotations as proxies for experienced emotion over time. Furthermore, we investigate whether weakly-
supervised models can generate accurate continuous annotations to reduce the annotation burden for large datasets. Our results indicate that external annotation sequences bear little significant information for the prediction of self-reports. However, forcing models to reflect changes in external annotations by training models in a multitask fashion improves model performance, suggesting that such temporal supervision helps models to distinguish relevant segments in input data. Besides this, we find that weakly-
supervised models can to a certain extent capture changes over time, but in general yield poor results compared to fully-
supervised models.
Advisors/Committee Members: Hung, H.S. (mentor), Oertel Genannt Bierbach, C.R.M.M. (graduation committee), Hildebrandt, K.A. (graduation committee), Dudzik, B.J.W. (graduation committee), Gudi, A.A. (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Affective Computing; Emotion Classification; Self-reports; Machine Learning; Weakly Supervised Learning
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APA (6th Edition):
Rietveld, T. M. (. (2020). The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:d9ecc6f2-19ab-4dc4-b3b4-513c7285677d
Chicago Manual of Style (16th Edition):
Rietveld, T M (author). “The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features.” 2020. Masters Thesis, Delft University of Technology. Accessed January 23, 2021.
http://resolver.tudelft.nl/uuid:d9ecc6f2-19ab-4dc4-b3b4-513c7285677d.
MLA Handbook (7th Edition):
Rietveld, T M (author). “The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features.” 2020. Web. 23 Jan 2021.
Vancouver:
Rietveld TM(. The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 23].
Available from: http://resolver.tudelft.nl/uuid:d9ecc6f2-19ab-4dc4-b3b4-513c7285677d.
Council of Science Editors:
Rietveld TM(. The Effect of Temporal Supervision on the Prediction of Self-reported Emotion from Behavioural Features. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:d9ecc6f2-19ab-4dc4-b3b4-513c7285677d
Carnegie Mellon University
28.
Zhang, Yi.
Learning with Limited Supervision by Input and Output Coding.
Degree: 2012, Carnegie Mellon University
URL: http://repository.cmu.edu/dissertations/156
► In many real-world applications of supervised learning, only a limited number of labeled examples are available because the cost of obtaining high-quality examples is high.…
(more)
▼ In many real-world applications of supervised learning, only a limited number of labeled examples are available because the cost of obtaining high-quality examples is high. Even with a relatively large number of labeled examples, the learning problem may still suffer from limited supervision as the complexity of the prediction function increases. Therefore, learning with limited supervision presents a major challenge to machine learning. With the goal of supervision reduction, this thesis studies the representation, discovery and incorporation of extra input and output information in learning.
Information about the input space can be encoded by regularization. We first design a semi-supervised learning method for text classification that encodes the correlation of words inferred from seemingly irrelevant unlabeled text. We then propose a multi-task learning framework with a matrix-normal penalty, which compactly encodes the covariance structure of the joint input space of multiple tasks. To capture structure information that is more general than covariance and correlation, we study a class of regularization penalties on model compressibility. Then we design the projection penalty, which encodes the structure information from a dimension reduction while controlling the risk of information loss.
Information about the output space can be exploited by error correcting output codes. Using the composite likelihood view, we propose an improved pairwise coding for multi-label classification, which encodes pairwise label density (as opposed to label comparisons) and decodes using variational methods. We then investigate problemdependent codes, where the encoding is learned from data instead of being predefined. We first propose a multi-label output code using canonical correlation analysis, where predictability of the code is optimized. We then argue that both discriminability and predictability are critical for output coding, and propose a max-margin formulation that promotes both discriminative and predictable codes.
We empirically study our methods in a wide spectrum of applications, including document categorization, landmine detection, face recognition, brain signal classification, handwritten digit recognition, house price forecasting, music emotion prediction, medical decision, email analysis, gene function classification, outdoor scene recognition, and so forth. In all these applications, our proposed methods for encoding input and output information lead to significantly improved prediction performance.
Subjects/Keywords: regularization; error-correcting output codes; supervised learning; semi-supervised learning; multi-task learning; multi-label classification; dimensionality reduction; Computer Sciences
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APA (6th Edition):
Zhang, Y. (2012). Learning with Limited Supervision by Input and Output Coding. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/156
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Zhang, Yi. “Learning with Limited Supervision by Input and Output Coding.” 2012. Thesis, Carnegie Mellon University. Accessed January 23, 2021.
http://repository.cmu.edu/dissertations/156.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Zhang, Yi. “Learning with Limited Supervision by Input and Output Coding.” 2012. Web. 23 Jan 2021.
Vancouver:
Zhang Y. Learning with Limited Supervision by Input and Output Coding. [Internet] [Thesis]. Carnegie Mellon University; 2012. [cited 2021 Jan 23].
Available from: http://repository.cmu.edu/dissertations/156.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Zhang Y. Learning with Limited Supervision by Input and Output Coding. [Thesis]. Carnegie Mellon University; 2012. Available from: http://repository.cmu.edu/dissertations/156
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
29.
Gutiérrez, Victor Antonio Laguna.
Classificação semi-supervisionada baseada em desacordo por similaridade.
Degree: Mestrado, Ciências de Computação e Matemática Computacional, 2010, University of São Paulo
URL: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21062010-142145/
;
► O aprendizado semi-supervisionado é um paradigma do aprendizado de máquina no qual a hipótese é induzida aproveitando tanto os dados rotulados quantos os dados não…
(more)
▼ O aprendizado semi-supervisionado é um paradigma do aprendizado de máquina no qual a hipótese é induzida aproveitando tanto os dados rotulados quantos os dados não rotulados. Este paradigma é particularmente útil quando a quantidade de exemplos rotulados é muito pequena e a rotulação manual dos exemplos é uma tarefa muito custosa. Nesse contexto, foi proposto o algoritmo Cotraining, que é um algoritmo muito utilizado no cenário semi-supervisionado, especialmente quando existe mais de uma visão dos dados. Esta característica do algoritmo Cotraining faz com que a sua aplicabilidade seja restrita a domínios multi-visão, o que diminui muito o potencial do algoritmo para resolver problemas reais. Nesta dissertação, é proposto o algoritmo Co2KNN, que é uma versão mono-visão do algoritmo Cotraining na qual, ao invés de combinar duas visões dos dados, combina duas estratégias diferentes de induzir classificadores utilizando a mesma visão dos dados. Tais estratégias são chamados de k-vizinhos mais próximos (KNN) Local e Global. No KNN Global, a vizinhança utilizada para predizer o rótulo de um exemplo não rotulado é conformada por aqueles exemplos que contém o novo exemplo entre os seus k vizinhos mais próximos. Entretanto, o KNN Local considera a estratégia tradicional do KNN para recuperar a vizinhança de um novo exemplo. A teoria do Aprendizado Semi-supervisionado Baseado em Desacordo foi utilizada para definir a base teórica do algoritmo Co2KNN, pois argumenta que para o sucesso do algoritmo Cotraining, é suficiente que os classificadores mantenham um grau de desacordo que permita o processo de aprendizado conjunto. Para avaliar o desempenho do Co2KNN, foram executados diversos experimentos que sugerem que o algoritmo Co2KNN tem melhor performance que diferentes algoritmos do estado da arte, especificamente, em domínios mono-visão. Adicionalmente, foi proposto um algoritmo otimizado para diminuir a complexidade computacional do KNN Global, permitindo o uso do Co2KNN em problemas reais de classificação
Semi-supervised learning is a machine learning paradigm in which the induced hypothesis is improved by taking advantage of unlabeled data. Semi-supervised learning is particularly useful when labeled data is scarce and difficult to obtain. In this context, the Cotraining algorithm was proposed. Cotraining is a widely used semisupervised approach that assumes the availability of two independent views of the data. In most real world scenarios, the multi-view assumption is highly restrictive, impairing its usability for classifification purposes. In this work, we propose the Co2KNN algorithm, which is a one-view Cotraining approach that combines two different k-Nearest Neighbors (KNN) strategies referred to as global and local k-Nearest Neighbors. In the global KNN, the nearest neighbors used to classify a new instance are given by the set of training examples which contains this instance within its k-nearest neighbors. In the local KNN, on the other hand, the neighborhood considered to classify a new instance is the set of…
Advisors/Committee Members: Lopes, Alneu de Andrade.
Subjects/Keywords: Aprendizado baseado em desacordo; Aprendizado semi-supervisionado; Classificação; Classification; Contraining; Cotraining; Semi-supervised leaning; Semi-supervised learning based in disagreement
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APA ·
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APA (6th Edition):
Gutiérrez, V. A. L. (2010). Classificação semi-supervisionada baseada em desacordo por similaridade. (Masters Thesis). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21062010-142145/ ;
Chicago Manual of Style (16th Edition):
Gutiérrez, Victor Antonio Laguna. “Classificação semi-supervisionada baseada em desacordo por similaridade.” 2010. Masters Thesis, University of São Paulo. Accessed January 23, 2021.
http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21062010-142145/ ;.
MLA Handbook (7th Edition):
Gutiérrez, Victor Antonio Laguna. “Classificação semi-supervisionada baseada em desacordo por similaridade.” 2010. Web. 23 Jan 2021.
Vancouver:
Gutiérrez VAL. Classificação semi-supervisionada baseada em desacordo por similaridade. [Internet] [Masters thesis]. University of São Paulo; 2010. [cited 2021 Jan 23].
Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21062010-142145/ ;.
Council of Science Editors:
Gutiérrez VAL. Classificação semi-supervisionada baseada em desacordo por similaridade. [Masters Thesis]. University of São Paulo; 2010. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21062010-142145/ ;
30.
Znaidia, Amel.
Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images.
Degree: Docteur es, Computer science, 2014, Châtenay-Malabry, Ecole centrale de Paris
URL: http://www.theses.fr/2014ECAP0017
► La présente thèse s’intéresse à l’annotation multimodale d’images dans le contexte des médias sociaux. Notre objectif est de combiner les modalités visuelles et textuelles (tags)…
(more)
▼ La présente thèse s’intéresse à l’annotation multimodale d’images dans le contexte des médias sociaux. Notre objectif est de combiner les modalités visuelles et textuelles (tags) afin d’améliorer les performances d’annotation d’images. Cependant, ces tags sont généralement issus d’une indexation personnelle, fournissant une information imparfaite et partiellement pertinente pour un objectif de description du contenu sémantique de l’image. En outre, en combinant les scores de prédiction de différents classifieurs appris sur les différentes modalités, l’annotation multimodale d’image fait face à leurs imperfections: l’incertitude, l’imprécision et l’incomplétude. Dans cette thèse, nous considérons que l’annotation multimodale d’image est soumise à ces imperfections à deux niveaux : niveau représentation et niveau décision. Inspiré de la théorie de fusion de l’information, nous concentrons nos efforts dans cette thèse sur la définition, l’identification et la prise en compte de ces aspects d’imperfections afin d’améliorer l’annotation d’images.
This thesis deals with multimodal image annotation in the context of social media. We seek to take advantage of textual (tags) and visual information in order to enhance the image annotation performances. However, these tags are often noisy, overly personalized and only a few of them are related to the semantic visual content of the image. In addition, when combining prediction scores from different classifiers learned on different modalities, multimodal image annotation faces their imperfections (uncertainty, imprecision and incompleteness). Consequently, we consider that multimodal image annotation is subject to imperfections at two levels: the representation and the decision. Inspired from the information fusion theory, we focus in this thesis on defining, identifying and handling imperfection aspects in order to improve image annotation.
Advisors/Committee Members: Paragios, Nikos (thesis director).
Subjects/Keywords: Annotation multimodale d’images; Classification supervisée d’images; Imperfections; Multimodal image annotation; Supervised image classification; Tag imperfections
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APA (6th Edition):
Znaidia, A. (2014). Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images. (Doctoral Dissertation). Châtenay-Malabry, Ecole centrale de Paris. Retrieved from http://www.theses.fr/2014ECAP0017
Chicago Manual of Style (16th Edition):
Znaidia, Amel. “Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images.” 2014. Doctoral Dissertation, Châtenay-Malabry, Ecole centrale de Paris. Accessed January 23, 2021.
http://www.theses.fr/2014ECAP0017.
MLA Handbook (7th Edition):
Znaidia, Amel. “Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images.” 2014. Web. 23 Jan 2021.
Vancouver:
Znaidia A. Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images. [Internet] [Doctoral dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2014. [cited 2021 Jan 23].
Available from: http://www.theses.fr/2014ECAP0017.
Council of Science Editors:
Znaidia A. Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images. [Doctoral Dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2014. Available from: http://www.theses.fr/2014ECAP0017
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Open Access Theses and Dissertations