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You searched for subject:(Semi supervised AND unsupervised extensions to structured prediction ). Showing records 1 – 30 of 72 total matches.

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University of Technology, Sydney

1. Abidi, Syed Shaukat Raza. Semi-supervised and unsupervised extensions to maximum-margin structured prediction.

Degree: 2016, University of Technology, Sydney

Structured prediction is the backbone of various computer vision and machine learning applications. Inspired by the success of maximum-margin classifiers in the recent years; in… (more)

Subjects/Keywords: Computer vision and machine learning applications.; Structured prediction.; Maximum-margin classifiers.; Semi-supervised and unsupervised extensions to structured prediction.; “Superpixels”.; Latent structural SVM approach.; Well-SSVM.; M³ Networks.; Interpretation of M³ Networks in Well-SSVM.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Abidi, S. S. R. (2016). Semi-supervised and unsupervised extensions to maximum-margin structured prediction. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/52925

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):

Abidi, Syed Shaukat Raza. “Semi-supervised and unsupervised extensions to maximum-margin structured prediction.” 2016. Thesis, University of Technology, Sydney. Accessed October 18, 2019. http://hdl.handle.net/10453/52925.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Abidi, Syed Shaukat Raza. “Semi-supervised and unsupervised extensions to maximum-margin structured prediction.” 2016. Web. 18 Oct 2019.

Vancouver:

Abidi SSR. Semi-supervised and unsupervised extensions to maximum-margin structured prediction. [Internet] [Thesis]. University of Technology, Sydney; 2016. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/10453/52925.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Abidi SSR. Semi-supervised and unsupervised extensions to maximum-margin structured prediction. [Thesis]. University of Technology, Sydney; 2016. Available from: http://hdl.handle.net/10453/52925

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Minnesota

2. Bermperidis, Dimitrios. Active and Adaptive Techniques for Learning over Large-Scale Graphs.

Degree: PhD, Electrical/Computer Engineering, 2019, University of Minnesota

 Behind every complex system be it physical, social, biological, or man-made, there is an intricate network encoding the interactions between its components. Learning over large-scale… (more)

Subjects/Keywords: Active Learning; Diffusions; Embedding; Scalable; Semi-supervised; Unsupervised

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APA (6th Edition):

Bermperidis, D. (2019). Active and Adaptive Techniques for Learning over Large-Scale Graphs. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206331

Chicago Manual of Style (16th Edition):

Bermperidis, Dimitrios. “Active and Adaptive Techniques for Learning over Large-Scale Graphs.” 2019. Doctoral Dissertation, University of Minnesota. Accessed October 18, 2019. http://hdl.handle.net/11299/206331.

MLA Handbook (7th Edition):

Bermperidis, Dimitrios. “Active and Adaptive Techniques for Learning over Large-Scale Graphs.” 2019. Web. 18 Oct 2019.

Vancouver:

Bermperidis D. Active and Adaptive Techniques for Learning over Large-Scale Graphs. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/11299/206331.

Council of Science Editors:

Bermperidis D. Active and Adaptive Techniques for Learning over Large-Scale Graphs. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206331


Université de Montréal

3. Pezeshki, Mohammad. Towards deep semi supervised learning .

Degree: 2017, Université de Montréal

 L'apprentissage profond est une sous-discipline de l'intelligence artificielle en plein essor grâce à d'impressionnantes performances, obtenue durant la dernière décennie, dans divers domaines d'application de… (more)

Subjects/Keywords: Neural Networks; Machine Learning; Deep Learning; Representation Learning; Unsupervised Learning; Supervised Learning; Semi-supervised Learning; Model Regularization; Réseaux de Neurones; Apprentissage Automatique; Apprentissage de Représentations Profondes; Apprentissage de Représentations; Apprentissage non Supervisé; Apprentissage Supervisé; Apprentissage Semi-supervisé; Régularisation

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APA (6th Edition):

Pezeshki, M. (2017). Towards deep semi supervised learning . (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/18343

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):

Pezeshki, Mohammad. “Towards deep semi supervised learning .” 2017. Thesis, Université de Montréal. Accessed October 18, 2019. http://hdl.handle.net/1866/18343.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pezeshki, Mohammad. “Towards deep semi supervised learning .” 2017. Web. 18 Oct 2019.

Vancouver:

Pezeshki M. Towards deep semi supervised learning . [Internet] [Thesis]. Université de Montréal; 2017. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1866/18343.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pezeshki M. Towards deep semi supervised learning . [Thesis]. Université de Montréal; 2017. Available from: http://hdl.handle.net/1866/18343

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

4. Benecke, Scott Robert. Bayes Neutral Zone Classification in Unsupervised and Semi-Supervised Settings.

Degree: Applied Statistics, 2012, University of California – Riverside

 Neutral zone classifiers allow for a region of neutrality when there is inadequate information to assign a predicted class label with suitable confidence. A neutral… (more)

Subjects/Keywords: Statistics; Bayesian; Classification; Neutral Zone; Nonparametric; Semi-supervised; Unsupervised

unsupervised classification. Semi-supervised contexts are where some of the training data has labels… …67 Table 3.5. Parametric semi-supervised classification Mahalanobis distance = .5… …71 Table 3.6. Parametric semi-supervised classification Mahalanobis distance = 1… …72 Table 3.7. Parametric semi-supervised classification Mahalanobis distance = 2… …73 Table 3.8. Parametric semi-supervised classification Mahalanobis distance = 4… 

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APA (6th Edition):

Benecke, S. R. (2012). Bayes Neutral Zone Classification in Unsupervised and Semi-Supervised Settings. (Thesis). University of California – Riverside. Retrieved from http://www.escholarship.org/uc/item/9v44r8ng

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):

Benecke, Scott Robert. “Bayes Neutral Zone Classification in Unsupervised and Semi-Supervised Settings.” 2012. Thesis, University of California – Riverside. Accessed October 18, 2019. http://www.escholarship.org/uc/item/9v44r8ng.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Benecke, Scott Robert. “Bayes Neutral Zone Classification in Unsupervised and Semi-Supervised Settings.” 2012. Web. 18 Oct 2019.

Vancouver:

Benecke SR. Bayes Neutral Zone Classification in Unsupervised and Semi-Supervised Settings. [Internet] [Thesis]. University of California – Riverside; 2012. [cited 2019 Oct 18]. Available from: http://www.escholarship.org/uc/item/9v44r8ng.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Benecke SR. Bayes Neutral Zone Classification in Unsupervised and Semi-Supervised Settings. [Thesis]. University of California – Riverside; 2012. Available from: http://www.escholarship.org/uc/item/9v44r8ng

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Waterloo

5. Huang, Jiayuan. Learning from Partially Labeled Data: Unsupervised and Semi-supervised Learning on Graphs and Learning with Distribution Shifting.

Degree: 2007, University of Waterloo

 This thesis focuses on two fundamental machine learning problems:unsupervised learning, where no label information is available, and semi-supervised learning, where a small amount of labels… (more)

Subjects/Keywords: unsupervised learning; semi-supervised learning; graph based learning; distribution shifting

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Huang, J. (2007). Learning from Partially Labeled Data: Unsupervised and Semi-supervised Learning on Graphs and Learning with Distribution Shifting. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/3165

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):

Huang, Jiayuan. “Learning from Partially Labeled Data: Unsupervised and Semi-supervised Learning on Graphs and Learning with Distribution Shifting.” 2007. Thesis, University of Waterloo. Accessed October 18, 2019. http://hdl.handle.net/10012/3165.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Huang, Jiayuan. “Learning from Partially Labeled Data: Unsupervised and Semi-supervised Learning on Graphs and Learning with Distribution Shifting.” 2007. Web. 18 Oct 2019.

Vancouver:

Huang J. Learning from Partially Labeled Data: Unsupervised and Semi-supervised Learning on Graphs and Learning with Distribution Shifting. [Internet] [Thesis]. University of Waterloo; 2007. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/10012/3165.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huang J. Learning from Partially Labeled Data: Unsupervised and Semi-supervised Learning on Graphs and Learning with Distribution Shifting. [Thesis]. University of Waterloo; 2007. Available from: http://hdl.handle.net/10012/3165

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Waterloo

6. Pal, David. Contributions to Unsupervised and Semi-Supervised Learning.

Degree: 2009, University of Waterloo

 This thesis studies two problems in theoretical machine learning. The first part of the thesis investigates the statistical stability of clustering algorithms. In the second… (more)

Subjects/Keywords: machine learning; statistics; unsupervised learning; semi-supervised learning; learning theory

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APA (6th Edition):

Pal, D. (2009). Contributions to Unsupervised and Semi-Supervised Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/4445

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):

Pal, David. “Contributions to Unsupervised and Semi-Supervised Learning.” 2009. Thesis, University of Waterloo. Accessed October 18, 2019. http://hdl.handle.net/10012/4445.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pal, David. “Contributions to Unsupervised and Semi-Supervised Learning.” 2009. Web. 18 Oct 2019.

Vancouver:

Pal D. Contributions to Unsupervised and Semi-Supervised Learning. [Internet] [Thesis]. University of Waterloo; 2009. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/10012/4445.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pal D. Contributions to Unsupervised and Semi-Supervised Learning. [Thesis]. University of Waterloo; 2009. Available from: http://hdl.handle.net/10012/4445

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Southern California

7. Chu, Selina. Recognition and characterization of unstructured environmental sounds.

Degree: PhD, Computer Science, 2011, University of Southern California

 Environmental sounds are what we hear everyday, or more generally sounds that surround us – ambient or background audio. Human utilize both vision and hearing… (more)

Subjects/Keywords: unstructured audio classification; auditory scene recognition; environmental sounds; background modeling; semi-supervised learning; deep belief networks; unsupervised feature learning; generalization; data representation; feature extraction; feature selection; MFCC; matching pursuit

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APA (6th Edition):

Chu, S. (2011). Recognition and characterization of unstructured environmental sounds. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/477025/rec/5440

Chicago Manual of Style (16th Edition):

Chu, Selina. “Recognition and characterization of unstructured environmental sounds.” 2011. Doctoral Dissertation, University of Southern California. Accessed October 18, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/477025/rec/5440.

MLA Handbook (7th Edition):

Chu, Selina. “Recognition and characterization of unstructured environmental sounds.” 2011. Web. 18 Oct 2019.

Vancouver:

Chu S. Recognition and characterization of unstructured environmental sounds. [Internet] [Doctoral dissertation]. University of Southern California; 2011. [cited 2019 Oct 18]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/477025/rec/5440.

Council of Science Editors:

Chu S. Recognition and characterization of unstructured environmental sounds. [Doctoral Dissertation]. University of Southern California; 2011. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/477025/rec/5440


University of Manchester

8. Rostamniakankalhori, Sharareh. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.

Degree: 2011, University of Manchester

 Tuberculosis (TB) is an infectious disease which is a global public health problem with over 9 million new cases annually. Tuberculosis treatment, with patient supervision… (more)

Subjects/Keywords: Integrated Supervised and Unsupervised Learning; Tuberculosis; plediction

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APA (6th Edition):

Rostamniakankalhori, S. (2011). Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404

Chicago Manual of Style (16th Edition):

Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.” 2011. Doctoral Dissertation, University of Manchester. Accessed October 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.

MLA Handbook (7th Edition):

Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.” 2011. Web. 18 Oct 2019.

Vancouver:

Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. [Internet] [Doctoral dissertation]. University of Manchester; 2011. [cited 2019 Oct 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.

Council of Science Editors:

Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. [Doctoral Dissertation]. University of Manchester; 2011. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404

9. OLIVEIRA, Paulo César de. Abordagem semi-supervisionada para detecção de módulos de software defeituosos .

Degree: 2015, Universidade Federal de Pernambuco

 Com a competitividade cada vez maior do mercado, aplicações de alto nível de qualidade são exigidas para a automação de um serviço. Para garantir qualidade… (more)

Subjects/Keywords: Aprendizagem de Máquina; Detecção de Módulos de Software Defeituosos; Aprendizagem Semi-Supervisionada; Aprendizagem Não Supervisionada; Teste de Software; Detecção de Anomalias; Machine Learning; Software Defect Detection; Semi-Supervised Learning; Unsupervised Learning; Software Testing; Anomaly Detection

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APA (6th Edition):

OLIVEIRA, P. C. d. (2015). Abordagem semi-supervisionada para detecção de módulos de software defeituosos . (Thesis). Universidade Federal de Pernambuco. Retrieved from http://repositorio.ufpe.br/handle/123456789/19990

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):

OLIVEIRA, Paulo César de. “Abordagem semi-supervisionada para detecção de módulos de software defeituosos .” 2015. Thesis, Universidade Federal de Pernambuco. Accessed October 18, 2019. http://repositorio.ufpe.br/handle/123456789/19990.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

OLIVEIRA, Paulo César de. “Abordagem semi-supervisionada para detecção de módulos de software defeituosos .” 2015. Web. 18 Oct 2019.

Vancouver:

OLIVEIRA PCd. Abordagem semi-supervisionada para detecção de módulos de software defeituosos . [Internet] [Thesis]. Universidade Federal de Pernambuco; 2015. [cited 2019 Oct 18]. Available from: http://repositorio.ufpe.br/handle/123456789/19990.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

OLIVEIRA PCd. Abordagem semi-supervisionada para detecção de módulos de software defeituosos . [Thesis]. Universidade Federal de Pernambuco; 2015. Available from: http://repositorio.ufpe.br/handle/123456789/19990

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Université de Montréal

10. Wood, Sean. Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals .

Degree: 2010, Université de Montréal

 On étudie l’application des algorithmes de décomposition matricielles tel que la Factorisation Matricielle Non-négative (FMN), aux représentations fréquentielles de signaux audio musicaux. Ces algorithmes, dirigés… (more)

Subjects/Keywords: Apprentissage machine non-supervisé; Apprentissage machine semi-supervisé; Factorisation matricielle non-négative; Encodage parcimonieux; Extraction de l’information musicale; Détection de la hauteur de notes; Unsupervised machine learning; Semi-supervised machine learning; Non-negative matrix factorization; Sparse coding; Music information retrieval; Pitch detection

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APA (6th Edition):

Wood, S. (2010). Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals . (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/3769

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):

Wood, Sean. “Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals .” 2010. Thesis, Université de Montréal. Accessed October 18, 2019. http://hdl.handle.net/1866/3769.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wood, Sean. “Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals .” 2010. Web. 18 Oct 2019.

Vancouver:

Wood S. Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals . [Internet] [Thesis]. Université de Montréal; 2010. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1866/3769.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wood S. Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals . [Thesis]. Université de Montréal; 2010. Available from: http://hdl.handle.net/1866/3769

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Université de Montréal

11. Goodfellow, Ian. Deep learning of representations and its application to computer vision .

Degree: 2015, Université de Montréal

 L’objectif de cette thèse par articles est de présenter modestement quelques étapes du parcours qui mènera (on espère) à une solution générale du problème de… (more)

Subjects/Keywords: réseau de neurones; apprentissage profond; apprentissage non supervisé; apprentissage supervisé; apprentissage semi-supervisé; machines de Boltzmann; les modèles basés sur l’énergie; l’inference variationnel; l’apprentissage variationnel; le codage parcimonieux; neural networks; deep learning; unsupervised learning; supervised learning; semi-supervised learning; Boltzmann machines; energy-based models; variational inference; variational learning; sparse coding

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APA (6th Edition):

Goodfellow, I. (2015). Deep learning of representations and its application to computer vision . (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/11674

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):

Goodfellow, Ian. “Deep learning of representations and its application to computer vision .” 2015. Thesis, Université de Montréal. Accessed October 18, 2019. http://hdl.handle.net/1866/11674.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Goodfellow, Ian. “Deep learning of representations and its application to computer vision .” 2015. Web. 18 Oct 2019.

Vancouver:

Goodfellow I. Deep learning of representations and its application to computer vision . [Internet] [Thesis]. Université de Montréal; 2015. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/1866/11674.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Goodfellow I. Deep learning of representations and its application to computer vision . [Thesis]. Université de Montréal; 2015. Available from: http://hdl.handle.net/1866/11674

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Linköping University

12. Alirezaie, Marjan. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.

Degree: Computer and Information Science, 2011, Linköping University

  The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem… (more)

Subjects/Keywords: Machine Learning; Supervised Learning; Unsupervised Learning; Computer Sciences; Datavetenskap (datalogi)

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APA (6th Edition):

Alirezaie, M. (2011). Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086

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):

Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Thesis, Linköping University. Accessed October 18, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Web. 18 Oct 2019.

Vancouver:

Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Internet] [Thesis]. Linköping University; 2011. [cited 2019 Oct 18]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Thesis]. Linköping University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Western Australia

13. Colreavy, Erin Patricia. Unsupervised categorization : perceptual shift, strategy development, and general principles.

Degree: PhD, 2008, University of Western Australia

Unsupervised categorization is the task of classifying novel stimuli without external feedback or guidance, and is important for every day decisions such as deciding whether… (more)

Subjects/Keywords: Categorization (Psychology); Cognition; Perception; Unsupervised categorization; Supervised categorization

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APA (6th Edition):

Colreavy, E. P. (2008). Unsupervised categorization : perceptual shift, strategy development, and general principles. (Doctoral Dissertation). University of Western Australia. Retrieved from http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=10612&local_base=GEN01-INS01

Chicago Manual of Style (16th Edition):

Colreavy, Erin Patricia. “Unsupervised categorization : perceptual shift, strategy development, and general principles.” 2008. Doctoral Dissertation, University of Western Australia. Accessed October 18, 2019. http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=10612&local_base=GEN01-INS01.

MLA Handbook (7th Edition):

Colreavy, Erin Patricia. “Unsupervised categorization : perceptual shift, strategy development, and general principles.” 2008. Web. 18 Oct 2019.

Vancouver:

Colreavy EP. Unsupervised categorization : perceptual shift, strategy development, and general principles. [Internet] [Doctoral dissertation]. University of Western Australia; 2008. [cited 2019 Oct 18]. Available from: http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=10612&local_base=GEN01-INS01.

Council of Science Editors:

Colreavy EP. Unsupervised categorization : perceptual shift, strategy development, and general principles. [Doctoral Dissertation]. University of Western Australia; 2008. Available from: http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=10612&local_base=GEN01-INS01


University of Manchester

14. Rostamniakankalhori, Sharareh. Integrated supervised and unsupervised learning method to predict the outcome of tuberculosis treatment course.

Degree: PhD, 2011, University of Manchester

 Tuberculosis (TB) is an infectious disease which is a global public health problem with over 9 million new cases annually. Tuberculosis treatment, with patient supervision… (more)

Subjects/Keywords: 616.24; Integrated Supervised and Unsupervised Learning; Tuberculosis; plediction

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APA (6th Edition):

Rostamniakankalhori, S. (2011). Integrated supervised and unsupervised learning method to predict the outcome of tuberculosis treatment course. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/integrated-supervised-and-unsupervised-learning-method-to-predict-the-outcome-of-tuberculosis-treatment-course(8a2f7033-3c27-4c6e-a575-85b90a547086).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553278

Chicago Manual of Style (16th Edition):

Rostamniakankalhori, Sharareh. “Integrated supervised and unsupervised learning method to predict the outcome of tuberculosis treatment course.” 2011. Doctoral Dissertation, University of Manchester. Accessed October 18, 2019. https://www.research.manchester.ac.uk/portal/en/theses/integrated-supervised-and-unsupervised-learning-method-to-predict-the-outcome-of-tuberculosis-treatment-course(8a2f7033-3c27-4c6e-a575-85b90a547086).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553278.

MLA Handbook (7th Edition):

Rostamniakankalhori, Sharareh. “Integrated supervised and unsupervised learning method to predict the outcome of tuberculosis treatment course.” 2011. Web. 18 Oct 2019.

Vancouver:

Rostamniakankalhori S. Integrated supervised and unsupervised learning method to predict the outcome of tuberculosis treatment course. [Internet] [Doctoral dissertation]. University of Manchester; 2011. [cited 2019 Oct 18]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/integrated-supervised-and-unsupervised-learning-method-to-predict-the-outcome-of-tuberculosis-treatment-course(8a2f7033-3c27-4c6e-a575-85b90a547086).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553278.

Council of Science Editors:

Rostamniakankalhori S. Integrated supervised and unsupervised learning method to predict the outcome of tuberculosis treatment course. [Doctoral Dissertation]. University of Manchester; 2011. Available from: https://www.research.manchester.ac.uk/portal/en/theses/integrated-supervised-and-unsupervised-learning-method-to-predict-the-outcome-of-tuberculosis-treatment-course(8a2f7033-3c27-4c6e-a575-85b90a547086).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553278

15. Nallabolu, Adithya Reddy. Unsupervised Learning of Spatiotemporal Features by Video Completion.

Degree: MS, Electrical and Computer Engineering, 2017, Virginia Tech

 In this work, we present an unsupervised representation learning approach for learning rich spatiotemporal features from videos without the supervision from semantic labels. We propose… (more)

Subjects/Keywords: Representation Learning; Supervised; Unsupervised

…x29; Context prediction. Doersch et al. [7] trains a CNN to predict the relative… …prediction. Pathak et al. [49] trains a generative CNN model to generate the missing… …thus of great interests to develop unsupervised learning algorithms that can learn… …Background There have been extensive research efforts to develop unsupervised algorithms for… …surrogate task to learn powerful spatiotemproal features in an unsupervised manner. 8 Chapter 2… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Nallabolu, A. R. (2017). Unsupervised Learning of Spatiotemporal Features by Video Completion. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/79702

Chicago Manual of Style (16th Edition):

Nallabolu, Adithya Reddy. “Unsupervised Learning of Spatiotemporal Features by Video Completion.” 2017. Masters Thesis, Virginia Tech. Accessed October 18, 2019. http://hdl.handle.net/10919/79702.

MLA Handbook (7th Edition):

Nallabolu, Adithya Reddy. “Unsupervised Learning of Spatiotemporal Features by Video Completion.” 2017. Web. 18 Oct 2019.

Vancouver:

Nallabolu AR. Unsupervised Learning of Spatiotemporal Features by Video Completion. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/10919/79702.

Council of Science Editors:

Nallabolu AR. Unsupervised Learning of Spatiotemporal Features by Video Completion. [Masters Thesis]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/79702


Kansas State University

16. Varshney, Varun. Supervised and unsupervised learning for plant and crop row detection in precision agriculture.

Degree: MS, Department of Computing and Information Sciences, 2017, Kansas State University

 The goal of this research is to present a comparison between different clustering and segmentation techniques, both supervised and unsupervised, to detect plant and crop… (more)

Subjects/Keywords: precision agriculture; deep learning; machine learning; supervised; unsupervised

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APA (6th Edition):

Varshney, V. (2017). Supervised and unsupervised learning for plant and crop row detection in precision agriculture. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/35463

Chicago Manual of Style (16th Edition):

Varshney, Varun. “Supervised and unsupervised learning for plant and crop row detection in precision agriculture.” 2017. Masters Thesis, Kansas State University. Accessed October 18, 2019. http://hdl.handle.net/2097/35463.

MLA Handbook (7th Edition):

Varshney, Varun. “Supervised and unsupervised learning for plant and crop row detection in precision agriculture.” 2017. Web. 18 Oct 2019.

Vancouver:

Varshney V. Supervised and unsupervised learning for plant and crop row detection in precision agriculture. [Internet] [Masters thesis]. Kansas State University; 2017. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/2097/35463.

Council of Science Editors:

Varshney V. Supervised and unsupervised learning for plant and crop row detection in precision agriculture. [Masters Thesis]. Kansas State University; 2017. Available from: http://hdl.handle.net/2097/35463


University of Manchester

17. Mohammed, Danlami Abdulmalik. OBSTACLE DETECTION AND EMERGENCY EXIT SIGN RECOGNITION FOR AUTONOMOUS NAVIGATION USING CAMERA PHONE.

Degree: 2017, University of Manchester

 In this research work, we develop an obstacle detection and emergency exit sign recognition system on a mobile phone by extending the feature from accelerated… (more)

Subjects/Keywords: Feature Extraction; Feature Description; Colour Histogram; Probability Density Function; Image Classification; Image Analysis; Optical flow; Time to Contact Information; Autonomous Navigation; Mobile phone; image Processing; Supervised Learning; Unsupervised Learning; Image Segmentation.

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APA (6th Edition):

Mohammed, D. A. (2017). OBSTACLE DETECTION AND EMERGENCY EXIT SIGN RECOGNITION FOR AUTONOMOUS NAVIGATION USING CAMERA PHONE. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:310687

Chicago Manual of Style (16th Edition):

Mohammed, Danlami Abdulmalik. “OBSTACLE DETECTION AND EMERGENCY EXIT SIGN RECOGNITION FOR AUTONOMOUS NAVIGATION USING CAMERA PHONE.” 2017. Doctoral Dissertation, University of Manchester. Accessed October 18, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:310687.

MLA Handbook (7th Edition):

Mohammed, Danlami Abdulmalik. “OBSTACLE DETECTION AND EMERGENCY EXIT SIGN RECOGNITION FOR AUTONOMOUS NAVIGATION USING CAMERA PHONE.” 2017. Web. 18 Oct 2019.

Vancouver:

Mohammed DA. OBSTACLE DETECTION AND EMERGENCY EXIT SIGN RECOGNITION FOR AUTONOMOUS NAVIGATION USING CAMERA PHONE. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2019 Oct 18]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:310687.

Council of Science Editors:

Mohammed DA. OBSTACLE DETECTION AND EMERGENCY EXIT SIGN RECOGNITION FOR AUTONOMOUS NAVIGATION USING CAMERA PHONE. [Doctoral Dissertation]. University of Manchester; 2017. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:310687


University of Manchester

18. Mohammed, Abdulmalik. Obstacle detection and emergency exit sign recognition for autonomous navigation using camera phone.

Degree: PhD, 2017, University of Manchester

 In this research work, we develop an obstacle detection and emergency exit sign recognition system on a mobile phone by extending the feature from accelerated… (more)

Subjects/Keywords: 004; Mobile phone; image Processing; Image Analysis; Unsupervised Learning; Image Segmentation.; Supervised Learning; Autonomous Navigation; Probability Density Function; Optical flow; Feature Extraction; Feature Description; Time to Contact Information; Colour Histogram; Image Classification

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APA (6th Edition):

Mohammed, A. (2017). Obstacle detection and emergency exit sign recognition for autonomous navigation using camera phone. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/obstacle-detection-and-emergency-exit-sign-recognition-for-autonomous-navigation-using-camera-phone(e0224d89-e743-47a4-8c68-52f718457098).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.756832

Chicago Manual of Style (16th Edition):

Mohammed, Abdulmalik. “Obstacle detection and emergency exit sign recognition for autonomous navigation using camera phone.” 2017. Doctoral Dissertation, University of Manchester. Accessed October 18, 2019. https://www.research.manchester.ac.uk/portal/en/theses/obstacle-detection-and-emergency-exit-sign-recognition-for-autonomous-navigation-using-camera-phone(e0224d89-e743-47a4-8c68-52f718457098).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.756832.

MLA Handbook (7th Edition):

Mohammed, Abdulmalik. “Obstacle detection and emergency exit sign recognition for autonomous navigation using camera phone.” 2017. Web. 18 Oct 2019.

Vancouver:

Mohammed A. Obstacle detection and emergency exit sign recognition for autonomous navigation using camera phone. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2019 Oct 18]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/obstacle-detection-and-emergency-exit-sign-recognition-for-autonomous-navigation-using-camera-phone(e0224d89-e743-47a4-8c68-52f718457098).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.756832.

Council of Science Editors:

Mohammed A. Obstacle detection and emergency exit sign recognition for autonomous navigation using camera phone. [Doctoral Dissertation]. University of Manchester; 2017. Available from: https://www.research.manchester.ac.uk/portal/en/theses/obstacle-detection-and-emergency-exit-sign-recognition-for-autonomous-navigation-using-camera-phone(e0224d89-e743-47a4-8c68-52f718457098).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.756832


University of Cambridge

19. Bui, Thang Duc. Efficient deterministic approximate Bayesian inference for Gaussian process models.

Degree: PhD, 2018, University of Cambridge

 Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian… (more)

Subjects/Keywords: machine learning; Gaussian process; approximate inference; Bayesian statistics; supervised learning; unsupervised learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bui, T. D. (2018). Efficient deterministic approximate Bayesian inference for Gaussian process models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600

Chicago Manual of Style (16th Edition):

Bui, Thang Duc. “Efficient deterministic approximate Bayesian inference for Gaussian process models.” 2018. Doctoral Dissertation, University of Cambridge. Accessed October 18, 2019. https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600.

MLA Handbook (7th Edition):

Bui, Thang Duc. “Efficient deterministic approximate Bayesian inference for Gaussian process models.” 2018. Web. 18 Oct 2019.

Vancouver:

Bui TD. Efficient deterministic approximate Bayesian inference for Gaussian process models. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Oct 18]. Available from: https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600.

Council of Science Editors:

Bui TD. Efficient deterministic approximate Bayesian inference for Gaussian process models. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600


University of Cambridge

20. Bui, Thang Duc. Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models .

Degree: 2018, University of Cambridge

 Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian… (more)

Subjects/Keywords: machine learning; Gaussian process; approximate inference; Bayesian statistics; supervised learning; unsupervised learning

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APA (6th Edition):

Bui, T. D. (2018). Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models . (Thesis). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/273833

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):

Bui, Thang Duc. “Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models .” 2018. Thesis, University of Cambridge. Accessed October 18, 2019. https://www.repository.cam.ac.uk/handle/1810/273833.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bui, Thang Duc. “Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models .” 2018. Web. 18 Oct 2019.

Vancouver:

Bui TD. Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models . [Internet] [Thesis]. University of Cambridge; 2018. [cited 2019 Oct 18]. Available from: https://www.repository.cam.ac.uk/handle/1810/273833.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bui TD. Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models . [Thesis]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/273833

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Penn State University

21. Garg, Mayank. BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING.

Degree: 2017, Penn State University

 This thesis studies the battery state of charge (SOC) estimation and battery anomaly detection using machine learning technique. Classically, battery SOC is estimated using a… (more)

Subjects/Keywords: Battery SOC estimation; Battery anomaly detection; Machine learning; Supervised Learning; Unsupervised Learning

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APA (6th Edition):

Garg, M. (2017). BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING. (Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/14213mxg1042

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):

Garg, Mayank. “BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING.” 2017. Thesis, Penn State University. Accessed October 18, 2019. https://etda.libraries.psu.edu/catalog/14213mxg1042.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Garg, Mayank. “BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING.” 2017. Web. 18 Oct 2019.

Vancouver:

Garg M. BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING. [Internet] [Thesis]. Penn State University; 2017. [cited 2019 Oct 18]. Available from: https://etda.libraries.psu.edu/catalog/14213mxg1042.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Garg M. BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING. [Thesis]. Penn State University; 2017. Available from: https://etda.libraries.psu.edu/catalog/14213mxg1042

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


San Jose State University

22. Kasbekar, Priyanka. Network Alignment In Heterogeneous Social Networks.

Degree: MS, Computer Science, 2019, San Jose State University

  Online Social Networks (OSN) have numerous applications and an ever growing user base. This has led to users being a part of multiple social… (more)

Subjects/Keywords: Online Social Networks; Network Alignment; Supervised learning; Unsupervised learning; OS and Networks; Theory and Algorithms

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APA (6th Edition):

Kasbekar, P. (2019). Network Alignment In Heterogeneous Social Networks. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.3k8u-sgmn ; https://scholarworks.sjsu.edu/etd_projects/721

Chicago Manual of Style (16th Edition):

Kasbekar, Priyanka. “Network Alignment In Heterogeneous Social Networks.” 2019. Masters Thesis, San Jose State University. Accessed October 18, 2019. https://doi.org/10.31979/etd.3k8u-sgmn ; https://scholarworks.sjsu.edu/etd_projects/721.

MLA Handbook (7th Edition):

Kasbekar, Priyanka. “Network Alignment In Heterogeneous Social Networks.” 2019. Web. 18 Oct 2019.

Vancouver:

Kasbekar P. Network Alignment In Heterogeneous Social Networks. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2019 Oct 18]. Available from: https://doi.org/10.31979/etd.3k8u-sgmn ; https://scholarworks.sjsu.edu/etd_projects/721.

Council of Science Editors:

Kasbekar P. Network Alignment In Heterogeneous Social Networks. [Masters Thesis]. San Jose State University; 2019. Available from: https://doi.org/10.31979/etd.3k8u-sgmn ; https://scholarworks.sjsu.edu/etd_projects/721

23. Cupertino, Thiago Henrique. Machine learning via dynamical processes on complex networks.

Degree: PhD, Ciências de Computação e Matemática Computacional, 2013, University of São Paulo

Extracting useful knowledge from data sets is a key concept in modern information systems. Consequently, the need of efficient techniques to extract the desired knowledge… (more)

Subjects/Keywords: Aprendizado baseado em redes; Aprendizado de máquina; Aprendizado não supervisionado; Aprendizado semissupervisionado; Aprendizado supervisionado; Caminhada aleatória; Complex networks; Consensus time; Controle pontual; Dimensionality reduction; Dynamical processes; Estado estacionário; Forças de interação; Interacting forces; Limiting probabilities; Machine learning; Network-based learning; Plinning control; Probabilidades limite; Processos dinâmicos; Random walk; Redes complexas; Redução de dimensionalidade; Semi-supervised learning; Stationary states; Supervised learning; Tempo de consenso; Unsupervised learning

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APA (6th Edition):

Cupertino, T. H. (2013). Machine learning via dynamical processes on complex networks. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25032014-154520/ ;

Chicago Manual of Style (16th Edition):

Cupertino, Thiago Henrique. “Machine learning via dynamical processes on complex networks.” 2013. Doctoral Dissertation, University of São Paulo. Accessed October 18, 2019. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25032014-154520/ ;.

MLA Handbook (7th Edition):

Cupertino, Thiago Henrique. “Machine learning via dynamical processes on complex networks.” 2013. Web. 18 Oct 2019.

Vancouver:

Cupertino TH. Machine learning via dynamical processes on complex networks. [Internet] [Doctoral dissertation]. University of São Paulo; 2013. [cited 2019 Oct 18]. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25032014-154520/ ;.

Council of Science Editors:

Cupertino TH. Machine learning via dynamical processes on complex networks. [Doctoral Dissertation]. University of São Paulo; 2013. Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25032014-154520/ ;


Brno University of Technology

24. Stoika, Anastasiia. Případová studie na dolování z dat v jazyce Python .

Degree: 2019, Brno University of Technology

 Tato práce se zabývá základními koncepty a technikami procesu získávání znalostí z dat. Cílem práce je demonstrovat dostupné prostředky jazyka Python, které umožňují provádět jednotlivé… (more)

Subjects/Keywords: získavání znalostí z dat; datová analýza; detekce odlehlých hodnot; detekce podvodních transakcí; detekce anomalií; analýza odlehlých hodnot; učení bez učitele; učení s učitelem; kombinace učení s učitelem i bez; klasifikace; Bayesovská klasifikace; lokální faktor odlehlosti; předzpracování dat; čištění dat; KDD; knowledge discovery in databases; data mining; data analysis; outlier detection; anomaly detection; outlier analysis; detecting fraudulent transactions; unsupervised learning; supervised learning; semi-supervised learning; classification; Naive Bayes; Local Outlier Factor; Isolation Forest; data preprocessing; data cleaning

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APA (6th Edition):

Stoika, A. (2019). Případová studie na dolování z dat v jazyce Python . (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/180261

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):

Stoika, Anastasiia. “Případová studie na dolování z dat v jazyce Python .” 2019. Thesis, Brno University of Technology. Accessed October 18, 2019. http://hdl.handle.net/11012/180261.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Stoika, Anastasiia. “Případová studie na dolování z dat v jazyce Python .” 2019. Web. 18 Oct 2019.

Vancouver:

Stoika A. Případová studie na dolování z dat v jazyce Python . [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2019 Oct 18]. Available from: http://hdl.handle.net/11012/180261.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Stoika A. Případová studie na dolování z dat v jazyce Python . [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/180261

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


The Ohio State University

25. Mohiddin, Syed B. Development of novel unsupervised and supervised informatics methods for drug discovery applications.

Degree: PhD, Chemical Engineering, 2006, The Ohio State University

 As of 2002, the cost of discovering a new drug was nearly $802 million with a timeline of nearly 13.6 years. Despite the large investments… (more)

Subjects/Keywords: Engineering, Chemical; Unsupervised Classification; Supervised Classification; Principal Component Analysis; Partial Least Squares; Hierarchical K-means Clustering; Identifying Diverse Molecular Targets; Prediction of Toxicity/Activity of Chemicals

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APA (6th Edition):

Mohiddin, S. B. (2006). Development of novel unsupervised and supervised informatics methods for drug discovery applications. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1138385657

Chicago Manual of Style (16th Edition):

Mohiddin, Syed B. “Development of novel unsupervised and supervised informatics methods for drug discovery applications.” 2006. Doctoral Dissertation, The Ohio State University. Accessed October 18, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1138385657.

MLA Handbook (7th Edition):

Mohiddin, Syed B. “Development of novel unsupervised and supervised informatics methods for drug discovery applications.” 2006. Web. 18 Oct 2019.

Vancouver:

Mohiddin SB. Development of novel unsupervised and supervised informatics methods for drug discovery applications. [Internet] [Doctoral dissertation]. The Ohio State University; 2006. [cited 2019 Oct 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1138385657.

Council of Science Editors:

Mohiddin SB. Development of novel unsupervised and supervised informatics methods for drug discovery applications. [Doctoral Dissertation]. The Ohio State University; 2006. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1138385657


Dublin City University

26. Kerr, Gráinne. Computational analysis of gene expression data.

Degree: School of Computing, 2009, Dublin City University

 Gene expression is central to the function of living cells. While advances in sequencing and expression measurement technology over the past decade has greatly facilitated… (more)

Subjects/Keywords: Bioinformatics; Computer simulation; microarray data analysis; gene expression data; supervised and unsupervised clustering methods; graph theory

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APA (6th Edition):

Kerr, G. (2009). Computational analysis of gene expression data. (Thesis). Dublin City University. Retrieved from http://doras.dcu.ie/14837/

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):

Kerr, Gráinne. “Computational analysis of gene expression data.” 2009. Thesis, Dublin City University. Accessed October 18, 2019. http://doras.dcu.ie/14837/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kerr, Gráinne. “Computational analysis of gene expression data.” 2009. Web. 18 Oct 2019.

Vancouver:

Kerr G. Computational analysis of gene expression data. [Internet] [Thesis]. Dublin City University; 2009. [cited 2019 Oct 18]. Available from: http://doras.dcu.ie/14837/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kerr G. Computational analysis of gene expression data. [Thesis]. Dublin City University; 2009. Available from: http://doras.dcu.ie/14837/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Youngstown State University

27. Alam, Mohammad Tanveer. Image Classification for Remote Sensing Using Data-Mining Techniques.

Degree: MSin Mathematics, Department of Mathematics and Statistics, 2011, Youngstown State University

  Remote Sensing engages electromagnetic sensors to measure and monitor changes in the earth's surface and atmosphere. Remote Sensing Satellites are currently the fastest growing… (more)

Subjects/Keywords: Computer Science; Geographic Information Science; Remote Sensing; Image Classification; Remote Sensing; Datamining; unsupervised classification; supervised classification; LANDSAT; IKONOS

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Alam, M. T. (2011). Image Classification for Remote Sensing Using Data-Mining Techniques. (Masters Thesis). Youngstown State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ysu1313003161

Chicago Manual of Style (16th Edition):

Alam, Mohammad Tanveer. “Image Classification for Remote Sensing Using Data-Mining Techniques.” 2011. Masters Thesis, Youngstown State University. Accessed October 18, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1313003161.

MLA Handbook (7th Edition):

Alam, Mohammad Tanveer. “Image Classification for Remote Sensing Using Data-Mining Techniques.” 2011. Web. 18 Oct 2019.

Vancouver:

Alam MT. Image Classification for Remote Sensing Using Data-Mining Techniques. [Internet] [Masters thesis]. Youngstown State University; 2011. [cited 2019 Oct 18]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1313003161.

Council of Science Editors:

Alam MT. Image Classification for Remote Sensing Using Data-Mining Techniques. [Masters Thesis]. Youngstown State University; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ysu1313003161

28. Moarii, Matahi. Apprentissage de données génomiques multiples pour le diagnostic et le pronostic du cancer : Learning from multiple genomic information in cancer for diagnosis and prognosis.

Degree: Docteur es, Bio-informatique, 2015, Paris, ENMP

De nombreuses initiatives ont été mises en places pour caractériser d'un point de vue moléculaire de grandes cohortes de cancers à partir de diverses sources… (more)

Subjects/Keywords: Apprentissage supervisé; Apprentissage non-Supervisé; Données à grande dimension; Supervised Analysis; Unsupervised Analysis; High-Dimensional Data; 610.28

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Moarii, M. (2015). Apprentissage de données génomiques multiples pour le diagnostic et le pronostic du cancer : Learning from multiple genomic information in cancer for diagnosis and prognosis. (Doctoral Dissertation). Paris, ENMP. Retrieved from http://www.theses.fr/2015ENMP0086

Chicago Manual of Style (16th Edition):

Moarii, Matahi. “Apprentissage de données génomiques multiples pour le diagnostic et le pronostic du cancer : Learning from multiple genomic information in cancer for diagnosis and prognosis.” 2015. Doctoral Dissertation, Paris, ENMP. Accessed October 18, 2019. http://www.theses.fr/2015ENMP0086.

MLA Handbook (7th Edition):

Moarii, Matahi. “Apprentissage de données génomiques multiples pour le diagnostic et le pronostic du cancer : Learning from multiple genomic information in cancer for diagnosis and prognosis.” 2015. Web. 18 Oct 2019.

Vancouver:

Moarii M. Apprentissage de données génomiques multiples pour le diagnostic et le pronostic du cancer : Learning from multiple genomic information in cancer for diagnosis and prognosis. [Internet] [Doctoral dissertation]. Paris, ENMP; 2015. [cited 2019 Oct 18]. Available from: http://www.theses.fr/2015ENMP0086.

Council of Science Editors:

Moarii M. Apprentissage de données génomiques multiples pour le diagnostic et le pronostic du cancer : Learning from multiple genomic information in cancer for diagnosis and prognosis. [Doctoral Dissertation]. Paris, ENMP; 2015. Available from: http://www.theses.fr/2015ENMP0086


Louisiana State University

29. Johnson, Kurt. Estimating impervious surfaces from a small urban watershed in Baton Rouge, Louisiana, using LANDSAT thematic mapper imagery.

Degree: MS, Social and Behavioral Sciences, 2004, Louisiana State University

 Many urban areas are using estimations of impervious surfaces as a means for better environmental management. This is because research over the last two decades… (more)

Subjects/Keywords: subpixel analysis; environmental impacts; unsupervised classification; supervised classification

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Johnson, K. (2004). Estimating impervious surfaces from a small urban watershed in Baton Rouge, Louisiana, using LANDSAT thematic mapper imagery. (Masters Thesis). Louisiana State University. Retrieved from etd-04142004-140341 ; https://digitalcommons.lsu.edu/gradschool_theses/2888

Chicago Manual of Style (16th Edition):

Johnson, Kurt. “Estimating impervious surfaces from a small urban watershed in Baton Rouge, Louisiana, using LANDSAT thematic mapper imagery.” 2004. Masters Thesis, Louisiana State University. Accessed October 18, 2019. etd-04142004-140341 ; https://digitalcommons.lsu.edu/gradschool_theses/2888.

MLA Handbook (7th Edition):

Johnson, Kurt. “Estimating impervious surfaces from a small urban watershed in Baton Rouge, Louisiana, using LANDSAT thematic mapper imagery.” 2004. Web. 18 Oct 2019.

Vancouver:

Johnson K. Estimating impervious surfaces from a small urban watershed in Baton Rouge, Louisiana, using LANDSAT thematic mapper imagery. [Internet] [Masters thesis]. Louisiana State University; 2004. [cited 2019 Oct 18]. Available from: etd-04142004-140341 ; https://digitalcommons.lsu.edu/gradschool_theses/2888.

Council of Science Editors:

Johnson K. Estimating impervious surfaces from a small urban watershed in Baton Rouge, Louisiana, using LANDSAT thematic mapper imagery. [Masters Thesis]. Louisiana State University; 2004. Available from: etd-04142004-140341 ; https://digitalcommons.lsu.edu/gradschool_theses/2888


Mississippi State University

30. Sumarsono, Alex. Low rank and sparse representation for hyperspectral imagery analysis.

Degree: PhD, Electrical and Computer Engineering, 2015, Mississippi State University

  This dissertation develops new techniques employing the Low-rank and Sparse Representation approaches to improve the performance of state-of-the-art algorithms in hyperspectral image analysis. The… (more)

Subjects/Keywords: estimation of the number of signal sources; supervised classification; unsupervised classification; target detection; anomaly detection; LRR; LRSR

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sumarsono, A. (2015). Low rank and sparse representation for hyperspectral imagery analysis. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-10222015-201436/ ;

Chicago Manual of Style (16th Edition):

Sumarsono, Alex. “Low rank and sparse representation for hyperspectral imagery analysis.” 2015. Doctoral Dissertation, Mississippi State University. Accessed October 18, 2019. http://sun.library.msstate.edu/ETD-db/theses/available/etd-10222015-201436/ ;.

MLA Handbook (7th Edition):

Sumarsono, Alex. “Low rank and sparse representation for hyperspectral imagery analysis.” 2015. Web. 18 Oct 2019.

Vancouver:

Sumarsono A. Low rank and sparse representation for hyperspectral imagery analysis. [Internet] [Doctoral dissertation]. Mississippi State University; 2015. [cited 2019 Oct 18]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-10222015-201436/ ;.

Council of Science Editors:

Sumarsono A. Low rank and sparse representation for hyperspectral imagery analysis. [Doctoral Dissertation]. Mississippi State University; 2015. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-10222015-201436/ ;

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