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Oregon State University
1.
Vatturi, Pavan Kumar.
Rare category detection using hierarchical mean shift.
Degree: MS, Computer Science, 2009, Oregon State University
URL: http://hdl.handle.net/1957/10191 
► Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant…
(more)
▼ Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of
machine learning that can address this issue using a "human-in-the-loop" approach. In this interactive setting, the algorithm asks the user to label a query data point under an existing category or declare the query data point to belong to a previously undiscovered category. The goal of category detection is to discover all the categories in the data in as few queries as possible. In a data set with imbalanced categories, the main challenge is in identifying the rare categories or anomalies; hence, the task is often referred to as rare category detection.
We present a new approach to rare category detection using a hierarchical mean shift procedure. In our approach, a hierarchy is created by repeatedly applying mean shift with increasing bandwidth on the entire data set. This hierarchy allows us to identify anomalies in the data set at different scales, which are then posed as queries to the user. The main advantage of this methodology over existing approaches is that it does not require any knowledge of the data set properties such as the total number of classes or the prior probabilities of the classes. Results on real-world data sets show that our hierarchical mean shift approach performs consistently better than previous techniques.
Advisors/Committee Members: Wong, Weng-Keen (advisor), Fern, Alan (committee member).
Subjects/Keywords: machine learning; Machine learning
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APA (6th Edition):
Vatturi, P. K. (2009). Rare category detection using hierarchical mean shift. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10191
Chicago Manual of Style (16th Edition):
Vatturi, Pavan Kumar. “Rare category detection using hierarchical mean shift.” 2009. Masters Thesis, Oregon State University. Accessed November 20, 2019.
http://hdl.handle.net/1957/10191.
MLA Handbook (7th Edition):
Vatturi, Pavan Kumar. “Rare category detection using hierarchical mean shift.” 2009. Web. 20 Nov 2019.
Vancouver:
Vatturi PK. Rare category detection using hierarchical mean shift. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1957/10191.
Council of Science Editors:
Vatturi PK. Rare category detection using hierarchical mean shift. [Masters Thesis]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/10191
Oregon State University
2.
Bao, Xinlong.
Applying machine learning for prediction, recommendation, and integration.
Degree: PhD, Computer Science, 2009, Oregon State University
URL: http://hdl.handle.net/1957/12549
► This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research…
(more)
▼ This dissertation explores the idea of applying
machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following three applications: FolderPredictor, Stacking Recommendation Engines, and Integrating
Learning and Reasoning.
FolderPredictor is an intelligent desktop software tool that helps the user quickly locate files on the computer. It predicts the file folder that the user will access next by applying
machine learning algorithms to the user's file access history. The predicted folders are presented in existing Windows GUIs, so that the user's cost for
learning new interactions is minimized. Multiple prediction algorithms are introduced and their performance is examined in two user studies.
Recommender systems are one of the most popular means of assisting internet users in finding useful online information. The second part of this dissertation presents a novel way of building hybrid recommender systems by applying the idea of Stacking from ensemble
learning. Properties of the input users/items, called runtime metrics, are employed as additional meta features to improve performance. The resulting system, called STREAM, outperforms each component engine and a static linear hybrid system in a movie recommendation problem.
Many desktop assistant systems help users better organize their electronic resources by incorporating
machine learning components (e.g., classifiers) to make intelligent predictions. The last part of this dissertation addresses the problem of how to improve the performance of these
learning components, by integrating
learning and reasoning through Markov logic. Through an inference engine called the PCE, multiple classifiers are integrated via a process called relational co-training that improves the performance of each classifier based on information propagated from other classifiers.
Advisors/Committee Members: Dietterich, Thomas G. (advisor), Bergman, Lawrence (committee member).
Subjects/Keywords: machine learning; Machine learning
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APA (6th Edition):
Bao, X. (2009). Applying machine learning for prediction, recommendation, and integration. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/12549
Chicago Manual of Style (16th Edition):
Bao, Xinlong. “Applying machine learning for prediction, recommendation, and integration.” 2009. Doctoral Dissertation, Oregon State University. Accessed November 20, 2019.
http://hdl.handle.net/1957/12549.
MLA Handbook (7th Edition):
Bao, Xinlong. “Applying machine learning for prediction, recommendation, and integration.” 2009. Web. 20 Nov 2019.
Vancouver:
Bao X. Applying machine learning for prediction, recommendation, and integration. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1957/12549.
Council of Science Editors:
Bao X. Applying machine learning for prediction, recommendation, and integration. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/12549
Oregon State University
3.
Liu, Liping.
Machine Learning Methods for Computational Sustainability.
Degree: PhD, Computer Science, 2016, Oregon State University
URL: http://hdl.handle.net/1957/59159
► Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these ecosystems are facing more and more pressure from human activities. Machine learning…
(more)
▼ Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these
ecosystems are facing more and more pressure from human activities.
Machine learning can
play an important role in promoting sustainability as a large amount of data is being collected
from ecosystems. There are at least three important and representative issues in the study of
sustainability: detecting the presence of species, modeling the distribution of species, and protecting
endangered species. For these three issues, this thesis selects three typical problems as
the main focus and studies these problems with different
machine learning techniques. Specifically,
this thesis investigates the problem of detecting bird species from bird song recordings,
the problem of modeling migrating birds at the population level, and the problem of designing a
conservation area for an endangered species. First, this thesis models the problem of bird song
classification as a weakly-supervised
learning problem and develops a probabilistic classification
model for the
learning problem. The thesis also analyzes the learnability of the superset label
learning problem to determine conditions under which one can learn a good classifier from the
training data. Second, the thesis models bird migration with a probabilistic graphical model at
the population level using a Collective Graphical Model (CGM). The thesis proposes a Gaussian
approximation to significantly improve the inference efficiency of the model. Theoretical results
show that the proposed Gaussian approximation is correct and can be calculated efficiently.
Third, the thesis studies a typical reserve design problem with a novel formulation of transductive
classification. Then the thesis solves the formulation with two optimization algorithms. The
learning techniques in this thesis are general and can also be applied to many other
machine
learning problems.
Advisors/Committee Members: Dietterich, Thomas (advisor), Fern, Xiaoli Z. (committee member).
Subjects/Keywords: machine learning; Machine learning
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APA (6th Edition):
Liu, L. (2016). Machine Learning Methods for Computational Sustainability. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/59159
Chicago Manual of Style (16th Edition):
Liu, Liping. “Machine Learning Methods for Computational Sustainability.” 2016. Doctoral Dissertation, Oregon State University. Accessed November 20, 2019.
http://hdl.handle.net/1957/59159.
MLA Handbook (7th Edition):
Liu, Liping. “Machine Learning Methods for Computational Sustainability.” 2016. Web. 20 Nov 2019.
Vancouver:
Liu L. Machine Learning Methods for Computational Sustainability. [Internet] [Doctoral dissertation]. Oregon State University; 2016. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1957/59159.
Council of Science Editors:
Liu L. Machine Learning Methods for Computational Sustainability. [Doctoral Dissertation]. Oregon State University; 2016. Available from: http://hdl.handle.net/1957/59159
Oregon State University
4.
Hooper, Samuel.
Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale.
Degree: MS, Geography, 2017, Oregon State University
URL: http://hdl.handle.net/1957/60148
► Developing accurate predictive distribution models requires adequately representing relevant spatial and temporal scales, as these scales are ultimately reflective of the relationships between distributions and…
(more)
▼ Developing accurate predictive distribution models requires adequately representing relevant spatial and temporal scales, as these scales are ultimately reflective of the relationships between distributions and influential environmental conditions. In this research, we considered both spatial and temporal scale and the influence each has on predicting broad-scale distributions of two disparate but related phenomena: land cover and bird distributions. Employing
machine-
learning algorithms, we first developed land cover time series datasets covering all of California, Oregon, and Washington with a model that simultaneously reflects local-scale heterogeneity and broad-scale homogeneity. We then used these and other land cover time series datasets to investigate the effects of temporal resolution on species distribution models.
In the second chapter, we focused on the importance of accurately representing the spatial scale of relationships between predictors and a response variable for broad-scale predictive models. Using both a novel
machine-
learning algorithm and a novel predictor dataset, we developed dense time series forest canopy cover (FCC) and impervious surface cover (ISC) datasets at a 30-meter spatial resolution for all of California, Oregon, and Washington. To develop both datasets, we employed a spatial ensemble modeling method using a population of locally defined and spatially overlapping decision trees, making it both appropriate at continental-scales and sensitive to local variation in predictor-response relationships. Our predictor variables were products of LandTrendr, a tool for developing time series images and derivatives from the Landsat archive. To develop the most accurate time series of FCC and ISC, we first tested two model parameters, sample size and estimator size. Using the best-performing configuration of each, we then compared our models with locally defined estimators to bagged decision trees, the most comparable model with globally defined estimators. Using the best-performing models and LandTrendr imagery, we developed yearly FCC and ISC maps, spanning 1990-2012. To test the temporal extensibility of our models, we compared our predicted 2011 maps to 2011 maps from the National Land Cover Database. We found that model performance for both FCC and ISC decreased with increasing estimator size and that models with locally defined estimators outperformed bagged decision trees. We also found that our models performed well when extending learned predictor-response relationships to predict 2011 FCC and ISC distributions. These results, in concert with several novel byproducts of the models that we developed, demonstrate that representing local-scale spatial relationships is critical to producing accurate broad-scale distribution models.
In the third chapter, we investigated the influence of temporal scale on an avian species distribution model (SDM) by comparing models developed with different temporal resolutions of land cover predictor data. We expressed temporal resolution as the time…
Advisors/Committee Members: Kennedy, Robert E. (advisor), Robinson, Douglas (committee member).
Subjects/Keywords: machine learning; Machine learning
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Manager
APA (6th Edition):
Hooper, S. (2017). Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/60148
Chicago Manual of Style (16th Edition):
Hooper, Samuel. “Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale.” 2017. Masters Thesis, Oregon State University. Accessed November 20, 2019.
http://hdl.handle.net/1957/60148.
MLA Handbook (7th Edition):
Hooper, Samuel. “Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale.” 2017. Web. 20 Nov 2019.
Vancouver:
Hooper S. Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale. [Internet] [Masters thesis]. Oregon State University; 2017. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1957/60148.
Council of Science Editors:
Hooper S. Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale. [Masters Thesis]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/60148
University of Georgia
5.
U, Man Chon.
Improving learning outcomes by using clustering validity analysis to reduce label uncertainty.
Degree: PhD, Computer Science, 2013, University of Georgia
URL: http://purl.galileo.usg.edu/uga_etd/u_man-chon_201308_phd
► When people make critical decisions, they often consider the opinions of multiple experts from different domains rather than committing themselves to a single expert or…
(more)
▼ When people make critical decisions, they often consider the opinions of multiple experts from different domains rather than committing themselves to a single expert or relying on their own judgment. The same principle applies in
machine learning for the construction of robust predictive models. However, the outcome of each expert/model might include a certain degree of uncertainty, which affects the robustness of the combined predictive model.
In this dissertation, we present two effective
machine learning frameworks (ROMP and VAMO) to tackle challenging problems in the domains of cancer prediction and malware clustering. Our frameworks successfully improve the
learning outcomes from multiple sources by using cluster validity analysis to reduce label uncertainty.
In ROMP, we introduce novel features for identifying cancer-causing mutations in the cancer kinome, and utilize various feature selection methods to evaluate our proposed features. We combine multiple classifiers for the prediction of rare oncogenic mutations, followed by using the Expectation Maximization (EM) clustering algorithm and our self-invented cluster validity metrics to improve the
learning outcomes from the ensemble classifier as well as to identify suspicious mutations in the dataset. Our framework successfully discovered rare causative mutations that were later confirmed by lab experiments.
Another framework – VAMO, provides a fully automated assessment of the quality of malware clustering results. VAMO does not require a manual mapping between malware family labels output by different AV scanners. Furthermore, VAMO does not discard malware samples for which a majority voting-based consensus cannot be reached. Instead, VAMO explicitly deals with the inconsistencies typical of multiple AV labels to build a more representative reference set. Our evaluation, which includes extensive experiments in a controlled setting and a real-world application, show that VAMO performs better than majority voting-based approaches, and provides a way for malware analysts to automatically assess the quality of their malware clustering results.
Advisors/Committee Members: Khaled Rasheed.
Subjects/Keywords: Machine Learning
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APA (6th Edition):
U, M. C. (2013). Improving learning outcomes by using clustering validity analysis to reduce label uncertainty. (Doctoral Dissertation). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/u_man-chon_201308_phd
Chicago Manual of Style (16th Edition):
U, Man Chon. “Improving learning outcomes by using clustering validity analysis to reduce label uncertainty.” 2013. Doctoral Dissertation, University of Georgia. Accessed November 20, 2019.
http://purl.galileo.usg.edu/uga_etd/u_man-chon_201308_phd.
MLA Handbook (7th Edition):
U, Man Chon. “Improving learning outcomes by using clustering validity analysis to reduce label uncertainty.” 2013. Web. 20 Nov 2019.
Vancouver:
U MC. Improving learning outcomes by using clustering validity analysis to reduce label uncertainty. [Internet] [Doctoral dissertation]. University of Georgia; 2013. [cited 2019 Nov 20].
Available from: http://purl.galileo.usg.edu/uga_etd/u_man-chon_201308_phd.
Council of Science Editors:
U MC. Improving learning outcomes by using clustering validity analysis to reduce label uncertainty. [Doctoral Dissertation]. University of Georgia; 2013. Available from: http://purl.galileo.usg.edu/uga_etd/u_man-chon_201308_phd
University of Georgia
6.
Richardson, William Dale.
Evolutionary instance resampling for difficult data sets.
Degree: MS, Artificial Intelligence, 2013, University of Georgia
URL: http://purl.galileo.usg.edu/uga_etd/richardson_william_d_201312_ms
► In the field of machine learning, properties of data sets such as class imbalance and overlap often pose difficulties for classifier algorithms. A number of…
(more)
▼ In the field of
machine learning, properties of data sets such as class imbalance and overlap often pose difficulties for classifier algorithms. A number of methods alleviate these difficulties by adjusting the distribution of the training data prior to classifier construction. Resampling is typically effected by weighting, removing, or duplicating instances, but finding a good resampling for the data set is a nontrivial problem. Genetic algorithms are frequently used to search for solutions in large, difficult search spaces. In this thesis, four evolutionary approaches are applied to the problem of instance resampling across a variety of data sets and classifier paradigms. In many cases, evolutionary pre-processing is able to produce better classifiers. In particular, an integer-based, one-to-one representation and a cluster-based, real-valued weighting encoding are shown to improve classifier performance on difficult data sets.
Advisors/Committee Members: Khaled Rasheed.
Subjects/Keywords: machine learning
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Manager
APA (6th Edition):
Richardson, W. D. (2013). Evolutionary instance resampling for difficult data sets. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/richardson_william_d_201312_ms
Chicago Manual of Style (16th Edition):
Richardson, William Dale. “Evolutionary instance resampling for difficult data sets.” 2013. Masters Thesis, University of Georgia. Accessed November 20, 2019.
http://purl.galileo.usg.edu/uga_etd/richardson_william_d_201312_ms.
MLA Handbook (7th Edition):
Richardson, William Dale. “Evolutionary instance resampling for difficult data sets.” 2013. Web. 20 Nov 2019.
Vancouver:
Richardson WD. Evolutionary instance resampling for difficult data sets. [Internet] [Masters thesis]. University of Georgia; 2013. [cited 2019 Nov 20].
Available from: http://purl.galileo.usg.edu/uga_etd/richardson_william_d_201312_ms.
Council of Science Editors:
Richardson WD. Evolutionary instance resampling for difficult data sets. [Masters Thesis]. University of Georgia; 2013. Available from: http://purl.galileo.usg.edu/uga_etd/richardson_william_d_201312_ms
University of Georgia
7.
Lyle, Arlo.
Baseball prediction using ensemble learning.
Degree: MS, Artificial Intelligence, 2007, University of Georgia
URL: http://purl.galileo.usg.edu/uga_etd/lyle_arlo_m_200705_ms
► As the salaries of baseball players continue to skyrocket and with the ever-increasing popularity of fantasy baseball, the desire for more accurate predictions of players’…
(more)
▼ As the salaries of baseball players continue to skyrocket and with the ever-increasing
popularity of fantasy baseball, the desire for more accurate predictions of players’ future
performances is building both for baseball executives and baseball fans. While most existing
work in performance prediction uses purely statistical methods, this thesis showcases research
in combining multiple
machine learning techniques to improve on current prediction systems
by increasing the accuracy of projections in several key offensive statistical categories. By
using the statistics of players from the past thirty years, the goal of this research is to more
accurately learn from this data how a player’s performance changes over time and apply
this knowledge to predicting future performance. Results have shown that using
machine
learning techniques to predict a player’s performance is comparable to the accuracy seen by
some of the best prediction systems currently available.
Advisors/Committee Members: Khaled Rasheed.
Subjects/Keywords: Machine Learning
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APA (6th Edition):
Lyle, A. (2007). Baseball prediction using ensemble learning. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/lyle_arlo_m_200705_ms
Chicago Manual of Style (16th Edition):
Lyle, Arlo. “Baseball prediction using ensemble learning.” 2007. Masters Thesis, University of Georgia. Accessed November 20, 2019.
http://purl.galileo.usg.edu/uga_etd/lyle_arlo_m_200705_ms.
MLA Handbook (7th Edition):
Lyle, Arlo. “Baseball prediction using ensemble learning.” 2007. Web. 20 Nov 2019.
Vancouver:
Lyle A. Baseball prediction using ensemble learning. [Internet] [Masters thesis]. University of Georgia; 2007. [cited 2019 Nov 20].
Available from: http://purl.galileo.usg.edu/uga_etd/lyle_arlo_m_200705_ms.
Council of Science Editors:
Lyle A. Baseball prediction using ensemble learning. [Masters Thesis]. University of Georgia; 2007. Available from: http://purl.galileo.usg.edu/uga_etd/lyle_arlo_m_200705_ms
Penn State University
8.
Gowda Aghalya Shyama Sundar, Deepika.
Identifying Product Web Pages Using Support Vector
Machines.
Degree: MS, Computer Science and Engineering, 2010, Penn State University
URL: https://etda.libraries.psu.edu/catalog/10299
► Comparative online shopping tools allow users to compare similar products from different vendors. Despite the availability of a multitude of online retail web sites, there…
(more)
▼ Comparative online shopping tools allow users to
compare similar products from different vendors. Despite the
availability of a multitude of online retail web sites, there is a
lack of effective comparative online search tools available for
consumer use. Currently, consumers who want to compare similar
products from different retail websites carry out the task by
searching individual websites. Effective algorithms that can
extract accurate product (as opposed to non-product) information
from different vendors and represent them on a comparative basis
have the potential to significantly reduce online shopping times.
As a first step towards building such a comparative tool (for any
product category), product web pages need to be identified. The
objective of this research is to develop and test algorithms to
identify product web pages among a collection of product and
non-product web pages. A typical web page can be identified by a
Uniform Resource Locator (URL) and contains text (user interface
data) and html code (user-hidden data which includes title tags,
anchor tags, head tags and body tags) that can be utilized to
classify web pages. The first algorithm is based on using URLs to
identify product web pages. The second algorithm proposes and tests
three methods of screening html information to create feature sets
as input data to the Support Vector Machine (SVM) algorithm. Each
feature set generated from the three techniques is given as input
to the SVM and the classification accuracy is determined. The
highest classification accuracy obtained determines the best Hyper
Text Markup Language (HTML) screening method to create the feature
set. The data set for the first algorithm consisted of seventy six
URLs from product and non product web pages of a commercial
computer vendor. The data set for the second algorithm consisted of
one hundred product and non-product web pages each from four
commercial vendors. The experimental result using the first
algorithm to identify certain web pages is promising, provided
there are valuable keywords in the URLs. Using the feature set
generated by method 3, the SVM based algorithm provided a good
classification accuracy of 93% and also reduced the learning phase
of the SVM algorithm. The thesis presents experimental results in
detail and also discusses the advantages and limitations of the
developed algorithms.
Subjects/Keywords: Machine Learning
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Manager
APA (6th Edition):
Gowda Aghalya Shyama Sundar, D. (2010). Identifying Product Web Pages Using Support Vector
Machines. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/10299
Chicago Manual of Style (16th Edition):
Gowda Aghalya Shyama Sundar, Deepika. “Identifying Product Web Pages Using Support Vector
Machines.” 2010. Masters Thesis, Penn State University. Accessed November 20, 2019.
https://etda.libraries.psu.edu/catalog/10299.
MLA Handbook (7th Edition):
Gowda Aghalya Shyama Sundar, Deepika. “Identifying Product Web Pages Using Support Vector
Machines.” 2010. Web. 20 Nov 2019.
Vancouver:
Gowda Aghalya Shyama Sundar D. Identifying Product Web Pages Using Support Vector
Machines. [Internet] [Masters thesis]. Penn State University; 2010. [cited 2019 Nov 20].
Available from: https://etda.libraries.psu.edu/catalog/10299.
Council of Science Editors:
Gowda Aghalya Shyama Sundar D. Identifying Product Web Pages Using Support Vector
Machines. [Masters Thesis]. Penn State University; 2010. Available from: https://etda.libraries.psu.edu/catalog/10299
University of California – San Diego
9.
Gallagher, Patrick W.
Operator Theory for Analysis of Convex Optimization Methods in Machine Learning.
Degree: Cognitive Science, 2014, University of California – San Diego
URL: http://www.escholarship.org/uc/item/153375qt
► As machine learning has more closely interacted with optimization, the concept of convexity has loomed large. Two properties beyond simple convexity have received particularly close…
(more)
▼ As machine learning has more closely interacted with optimization, the concept of convexity has loomed large. Two properties beyond simple convexity have received particularly close attention: strong smoothness and strong convexity. These properties (and their relatives) underlie machine learning analyses from convergence rates to generalization bounds – they are central and fundamental. This thesis takes as its focus properties from operator theory that, in specific instances, relate to broadened conceptions of convexity, strong smoothness, and strong convexity. Some of the properties we consider coincide with strong smoothness and strong convexity in some settings, but represent broadenings of these concepts in other situations of interest. Our intention throughout is to take an approach that balances theoretical generality with ease of use and subsequent extension. Through this approach we establish a framework, novel in its scope of application, in which a single analysis serves to recover standard convergence rates (typically established via a variety of separate arguments) for convex optimization methods prominent in machine learning. The framework is based on a perspective in which the iterative update for each convex optimization method is regarded as the application of some operator. We establish a collection of correspondences, novel in its comprehensiveness, that exist between "contractivity- type'' properties of the iterative update operator and "monotonicity-type'' properties of the associated displacement operator. We call particular attention to the comparison between the broader range of properties that we discuss and the more restricted range considered in the contemporary literature, demonstrating as well the relationship between the broader and narrower range. In support of our discussion of these property correspondences and the optimization method analyses based on them, we relate operator theory concepts that may be unfamiliar to a machine learning audience to more familiar concepts from convex analysis. In addition to grounding our discussion of operator theory, this turns out to provide a fresh perspective on many touchstone concepts from convex analysis
Subjects/Keywords: Machine learning
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APA (6th Edition):
Gallagher, P. W. (2014). Operator Theory for Analysis of Convex Optimization Methods in Machine Learning. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/153375qt
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):
Gallagher, Patrick W. “Operator Theory for Analysis of Convex Optimization Methods in Machine Learning.” 2014. Thesis, University of California – San Diego. Accessed November 20, 2019.
http://www.escholarship.org/uc/item/153375qt.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Gallagher, Patrick W. “Operator Theory for Analysis of Convex Optimization Methods in Machine Learning.” 2014. Web. 20 Nov 2019.
Vancouver:
Gallagher PW. Operator Theory for Analysis of Convex Optimization Methods in Machine Learning. [Internet] [Thesis]. University of California – San Diego; 2014. [cited 2019 Nov 20].
Available from: http://www.escholarship.org/uc/item/153375qt.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Gallagher PW. Operator Theory for Analysis of Convex Optimization Methods in Machine Learning. [Thesis]. University of California – San Diego; 2014. Available from: http://www.escholarship.org/uc/item/153375qt
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Victoria
10.
Lam, Newman Ming Ki.
Learning in the real world environment: a classification model based on sensitivity to within-dimension and between-category variation of feature frequencies.
Degree: School of Public Administration, 2018, University of Victoria
URL: https://dspace.library.uvic.ca//handle/1828/9498
► Research on machine learning has taken numerous different directions. The present study focussed on the microstructural characteristics of learning systems. It was postulated that learning…
(more)
▼ Research on
machine learning has taken numerous different
directions. The present study focussed on the microstructural
characteristics of
learning systems. It was
postulated that
learning systems consist of a macrostructure
which controls the flow of information, and a
micro-structure which manipulates information for decision
making. A review of the literature suggested that the basic
function of the micro-structure of
learning systems was to
make a choice among a set of alternatives. This decision
function was then equated with the task of making
classification decisions. On the basis of the requirements
for practical
learning systems, the feature frequency
approach was chosen for model development. An analysis of
the feature frequency approach indicated that an effective
model must be sensitive to both within-dimension and
between-category variations in frequencies. A model was
then developed to provide for such sensitivities. The model
was based on the Bayes' Theorem with an assumption of
uniform prior probability of occurrence for the categories.
This model was tested using data collected for
neuropsychological diagnosis of children. Results of the
tests showed that the model was capable of
learning and
provided a satisfactory level of performance. The
performance of the model was compared with that of other
models designed for the same purpose. The other models
included NEXSYS, a rule-based system specially design for
this type of diagnosis, discriminant analysis, which is a
statistical technique widely used for pattern recognition,
and neural networks, which attempt to simulate the neural
activities of the brain. Results of the tests showed that
the model's performance was comparable to that of the other
models. Further analysis indicated that the model has certain advantages in that it has a simple structure, is
capable of explaining its decisions, and is more efficient
than the other models.
Advisors/Committee Members: MacGregor, James (supervisor).
Subjects/Keywords: Machine learning
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APA (6th Edition):
Lam, N. M. K. (2018). Learning in the real world environment: a classification model based on sensitivity to within-dimension and between-category variation of feature frequencies. (Thesis). University of Victoria. Retrieved from https://dspace.library.uvic.ca//handle/1828/9498
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):
Lam, Newman Ming Ki. “Learning in the real world environment: a classification model based on sensitivity to within-dimension and between-category variation of feature frequencies.” 2018. Thesis, University of Victoria. Accessed November 20, 2019.
https://dspace.library.uvic.ca//handle/1828/9498.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lam, Newman Ming Ki. “Learning in the real world environment: a classification model based on sensitivity to within-dimension and between-category variation of feature frequencies.” 2018. Web. 20 Nov 2019.
Vancouver:
Lam NMK. Learning in the real world environment: a classification model based on sensitivity to within-dimension and between-category variation of feature frequencies. [Internet] [Thesis]. University of Victoria; 2018. [cited 2019 Nov 20].
Available from: https://dspace.library.uvic.ca//handle/1828/9498.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lam NMK. Learning in the real world environment: a classification model based on sensitivity to within-dimension and between-category variation of feature frequencies. [Thesis]. University of Victoria; 2018. Available from: https://dspace.library.uvic.ca//handle/1828/9498
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Oxford
11.
Bouchacourt, Diane.
Task-oriented learning of structured probability distributions.
Degree: PhD, 2017, University of Oxford
URL: https://ora.ox.ac.uk/objects/uuid:0665495b-afbb-483b-8bdf-cbc6ae5baeff
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740819
► Machine learning models automatically learn from historical data to predict unseen events. Such events are often represented as complex multi-dimensional structures. In many cases there…
(more)
▼ Machine learning models automatically learn from historical data to predict unseen events. Such events are often represented as complex multi-dimensional structures. In many cases there is high uncertainty in the prediction process. Research has developed probabilistic models to capture distributions of complex objects, but their learning objective is often agnostic of the evaluation loss. In this thesis, we address the aforementioned defficiency by designing probabilistic methods for structured object prediction that take into account the task at hand. First, we consider that the task at hand is explicitly known, but there is ambiguity in the prediction due to an unobserved (latent) variable. We develop a framework for latent structured output prediction that unifies existing empirical risk minimisation methods. We empirically demonstrate that for large and ambiguous latent spaces, performing prediction by minimising the uncertainty in the latent variable provides more accurate results. Empirical risk minimisation methods predict only a pointwise estimate of the output, however there can be uncertainty on the output value itself. To tackle this deficiency, we introduce a novel type of model to perform probabilistic structured output prediction. Our training objective minimises a dissimilarity coefficient between the data distribution and the model's distribution. This coefficient is defined according to a loss of choice, thereby our objective can be tailored to the task loss. We empirically demonstrate the ability of our model to capture distributions over complex objects. Finally, we tackle a setting where the task loss is implicitly expressed. Specifically, we consider the case of grouped observations. We propose a new model for learning a representation of the data that decomposes according to the semantics behind this grouping, while allowing efficient test-time inference. We experimentally demonstrate that our model learns a disentangled and controllable representation, leverages grouping information when available, and generalises to unseen observations.
Subjects/Keywords: Machine Learning
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Manager
APA (6th Edition):
Bouchacourt, D. (2017). Task-oriented learning of structured probability distributions. (Doctoral Dissertation). University of Oxford. Retrieved from https://ora.ox.ac.uk/objects/uuid:0665495b-afbb-483b-8bdf-cbc6ae5baeff ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740819
Chicago Manual of Style (16th Edition):
Bouchacourt, Diane. “Task-oriented learning of structured probability distributions.” 2017. Doctoral Dissertation, University of Oxford. Accessed November 20, 2019.
https://ora.ox.ac.uk/objects/uuid:0665495b-afbb-483b-8bdf-cbc6ae5baeff ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740819.
MLA Handbook (7th Edition):
Bouchacourt, Diane. “Task-oriented learning of structured probability distributions.” 2017. Web. 20 Nov 2019.
Vancouver:
Bouchacourt D. Task-oriented learning of structured probability distributions. [Internet] [Doctoral dissertation]. University of Oxford; 2017. [cited 2019 Nov 20].
Available from: https://ora.ox.ac.uk/objects/uuid:0665495b-afbb-483b-8bdf-cbc6ae5baeff ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740819.
Council of Science Editors:
Bouchacourt D. Task-oriented learning of structured probability distributions. [Doctoral Dissertation]. University of Oxford; 2017. Available from: https://ora.ox.ac.uk/objects/uuid:0665495b-afbb-483b-8bdf-cbc6ae5baeff ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740819
Oregon State University
12.
Das, Shubhomoy.
Incorporating User Feedback into Machine Learning Systems.
Degree: PhD, 2017, Oregon State University
URL: http://hdl.handle.net/1957/61580
► Although machine learning systems are often effective in real-world applications, there are situations in which they can be even better when provided with some degree…
(more)
▼ Although
machine learning systems are often effective in real-world applications, there are situations in which they can be even better when provided with some degree of end user feedback. This is especially true when the
machine learning system needs to customize itself to the end user's preferences, such as in a recommender system, an email classifier or an anomaly detector.
This thesis explores two directions in incorporating end user feedback to
machine learning systems. First, I introduce an algorithm that incorporates feature feedback in a semi-supervised text classification setting. Feature feedback goes beyond instance-label feedback by allowing end users to indicate which feature-value combinations are predictive of the class label. In order to incorporate feature feedback in a semi-supervised setting, I develop a Locally Weighted Logistic Regression algorithm that uses a similarity metric combining information from the user's feature feedback and information based on label diffusion on the unlabeled data.
Second, I explore the use of instance-level feedback to anomaly detection algorithms. Anomaly detectors commonly return a list of the top outliers in the data. Although these outliers are statistically unusual, some are uninteresting to a user as the internal statistical model may not necessarily be aligned with the user's semantic notion of an anomaly. I present an algorithm that can increase the number of true anomalies presented to the user if a limited amount of instances are labeled as anomalous or nominal.
Advisors/Committee Members: Wong, Weng-Keen (advisor), Dietterich, Thomas (committee member).
Subjects/Keywords: Machine Learning
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APA ·
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MLA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Das, S. (2017). Incorporating User Feedback into Machine Learning Systems. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/61580
Chicago Manual of Style (16th Edition):
Das, Shubhomoy. “Incorporating User Feedback into Machine Learning Systems.” 2017. Doctoral Dissertation, Oregon State University. Accessed November 20, 2019.
http://hdl.handle.net/1957/61580.
MLA Handbook (7th Edition):
Das, Shubhomoy. “Incorporating User Feedback into Machine Learning Systems.” 2017. Web. 20 Nov 2019.
Vancouver:
Das S. Incorporating User Feedback into Machine Learning Systems. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/1957/61580.
Council of Science Editors:
Das S. Incorporating User Feedback into Machine Learning Systems. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61580
University of Waikato
13.
Han, Zhimeng.
Smoothing in Probability Estimation Trees
.
Degree: 2011, University of Waikato
URL: http://hdl.handle.net/10289/5701
► Classification learning is a type of supervised machine learning technique that uses a classification model (e.g. decision tree) to predict unknown class labels for previously…
(more)
▼ Classification
learning is a type of supervised
machine learning technique that uses a classification model (e.g. decision tree) to predict unknown class labels for previously unseen instances. In many applications it can be very useful to additionally obtain class probabilities for the different class labels. Decision trees that yield these probabilities are also called probability estimation trees (PETs). Smoothing is a technique used to improve the probability estimates. There are several existing smoothing methods, such as the Laplace correction, M-Estimate smoothing and M-Branch smoothing. Smoothing does not just apply to PETs. In the field of text compression, PPM in particular, smoothing methods play a important role. This thesis migrates smoothing methods from text compression to PETs. The newly migrated methods in PETs are compared with the best of the existing smoothing methods considered in this thesis under different experiment setups. Unpruned, pruned and bagged trees are considered in the experiments. The main finding is that the PPM-based methods yield the best probability estimate when used with bagged trees, but not when used with individual (pruned or unpruned) trees.
Advisors/Committee Members: Frank, Eibe (advisor).
Subjects/Keywords: Machine Learning
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APA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Han, Z. (2011). Smoothing in Probability Estimation Trees
. (Masters Thesis). University of Waikato. Retrieved from http://hdl.handle.net/10289/5701
Chicago Manual of Style (16th Edition):
Han, Zhimeng. “Smoothing in Probability Estimation Trees
.” 2011. Masters Thesis, University of Waikato. Accessed November 20, 2019.
http://hdl.handle.net/10289/5701.
MLA Handbook (7th Edition):
Han, Zhimeng. “Smoothing in Probability Estimation Trees
.” 2011. Web. 20 Nov 2019.
Vancouver:
Han Z. Smoothing in Probability Estimation Trees
. [Internet] [Masters thesis]. University of Waikato; 2011. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/10289/5701.
Council of Science Editors:
Han Z. Smoothing in Probability Estimation Trees
. [Masters Thesis]. University of Waikato; 2011. Available from: http://hdl.handle.net/10289/5701
University of North Carolina – Greensboro
14.
Al-Hamadani, Mokhaled N.A.
Evaluation of the performance of deep learning techniques
over tampered dataset.
Degree: 2015, University of North Carolina – Greensboro
URL: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=18053
► The reduction of classification error over supervised data sets is the main goal in Deep Learning (DL) approaches. However, tampered data is a serious problem…
(more)
▼ The reduction of classification error over supervised
data sets is the main goal in Deep
Learning (DL) approaches.
However, tampered data is a serious problem in
machine learning
techniques. One of the recent interests to the
machine learning
community is the performance enhancement of supervised
learning
algorithms over tampered training data. In this thesis, the
well-known deep
learning techniques known as No-Drop, Dropout and
DropConnect have been investigated by using toy example data set,
the popular handwritten digits data set (MNIST), and our new
natural images data set. The investigation divided into three
groups which are training Deep
Learning techniques over regular
data sets, tampered data sets and noisy data sets. First, Deep
Learning techniques have been investigated over regular data sets,
the experiments showed good results in terms of accuracy and error
rate. Then, Deep
learning techniques were investigated with
tampered MNIST data, this tampered mechanism is the first step
toward the security analysis of Deep
Learning techniques. The
results of DL techniques over tampered MNIST data set showed the
same as in regular MNIST. Therefore, the investigation continued
with adding two noises which were Gaussian noise and Salt and
Pepper noise to reduce the clarity of the MNIST data set. The
results showed that Deep
Learning techniques still give good
accuracy under noise field environment. The thesis contribution is
the extensive research that supports Deep
Learning techniques that
trained over tampered data to obtain high classification accuracy.;
Deep
Learning, DropConnect, Dropout,
Machine Learning, Neural
Networks, No-Drop
Advisors/Committee Members: Shan Suthaharan (advisor).
Subjects/Keywords: Machine learning
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APA ·
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MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Al-Hamadani, M. N. A. (2015). Evaluation of the performance of deep learning techniques
over tampered dataset. (Masters Thesis). University of North Carolina – Greensboro. Retrieved from http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=18053
Chicago Manual of Style (16th Edition):
Al-Hamadani, Mokhaled N A. “Evaluation of the performance of deep learning techniques
over tampered dataset.” 2015. Masters Thesis, University of North Carolina – Greensboro. Accessed November 20, 2019.
http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=18053.
MLA Handbook (7th Edition):
Al-Hamadani, Mokhaled N A. “Evaluation of the performance of deep learning techniques
over tampered dataset.” 2015. Web. 20 Nov 2019.
Vancouver:
Al-Hamadani MNA. Evaluation of the performance of deep learning techniques
over tampered dataset. [Internet] [Masters thesis]. University of North Carolina – Greensboro; 2015. [cited 2019 Nov 20].
Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=18053.
Council of Science Editors:
Al-Hamadani MNA. Evaluation of the performance of deep learning techniques
over tampered dataset. [Masters Thesis]. University of North Carolina – Greensboro; 2015. Available from: http://libres.uncg.edu/ir/listing.aspx?styp=ti&id=18053
Wake Forest University
15.
Patel, Udita.
Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture.
Degree: 2016, Wake Forest University
URL: http://hdl.handle.net/10339/59315
► The quantity of electronic data available for analysis has grown exponentially with the rapid development of the World Wide Web, the Internet of Things, and…
(more)
▼ The quantity of electronic data available for analysis has grown exponentially with the rapid development of the World Wide Web, the Internet of Things, and other digital technologies. As a result, data mining and machine learning algorithms face computational complexity issues when applied to real world datasets. Support Vector Machines (SVM) are powerful classification and regression tools but their computational requirements increase rapidly as the number of training examples increases. To address this problem, several parallel MapReduce based implementations of SVMs have been proposed. These implementation have in common that they decompose a large-scale multi-class problem to a number of relatively smaller subproblems by dividing the data into multiple partitions which can be processed in parallel; however, these approaches use different aggregation and combination strategies to form the final model.
Subjects/Keywords: Machine Learning
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Manager
APA (6th Edition):
Patel, U. (2016). Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture. (Thesis). Wake Forest University. Retrieved from http://hdl.handle.net/10339/59315
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):
Patel, Udita. “Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture.” 2016. Thesis, Wake Forest University. Accessed November 20, 2019.
http://hdl.handle.net/10339/59315.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Patel, Udita. “Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture.” 2016. Web. 20 Nov 2019.
Vancouver:
Patel U. Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture. [Internet] [Thesis]. Wake Forest University; 2016. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/10339/59315.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Patel U. Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture. [Thesis]. Wake Forest University; 2016. Available from: http://hdl.handle.net/10339/59315
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Illinois – Urbana-Champaign
16.
Cai, Deng.
Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning.
Degree: PhD, Computer Science, 2009, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/11702
► Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. These methods use information contained in the eigenvectors of a…
(more)
▼ Spectral methods have recently emerged as a powerful tool for
dimensionality reduction and manifold
learning. These methods use
information contained in the eigenvectors of a data affinity (\ie,
item-item similarity) matrix to reveal the low dimensional structure
in the high dimensional data. The most popular manifold
learning algorithms
include Locally Linear Embedding, ISOMAP, and Laplacian Eigenmap.
However, these algorithms only provide the embedding results of
training samples. There are many extensions of these approaches
which try to solve the out-of-sample extension problem by seeking an
embedding function in reproducing kernel Hilbert space. However, a
disadvantage of all these approaches is that their computations
usually involve eigen-decomposition of dense matrices which is
expensive in both time and memory. In this thesis, we introduce a
novel dimensionality reduction framework, called { Spectral
Regression} (SR). SR casts the problem of
learning an embedding
function into a regression framework, which avoids
eigen-decomposition of dense matrices. Also, with the regression
as a building block, different kinds of regularizers can be naturally
incorporated into our framework which makes it more flexible. SR can
be performed in supervised, unsupervised and semi-supervised
situation. It can make efficient use of both labeled and unlabeled
points to discover the intrinsic discriminant structure in the data.
We have applied our algorithms to several real world applications,
e.g. face analysis, document representation and content-based image
retrieval.
Advisors/Committee Members: Han, Jiawei (Committee Chair), Huang, Thomas S. (committee member), Zhai, ChengXiang (committee member), Chang, Kevin C-C. (committee member).
Subjects/Keywords: Machine Learning
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APA ·
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MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Cai, D. (2009). Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/11702
Chicago Manual of Style (16th Edition):
Cai, Deng. “Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning.” 2009. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed November 20, 2019.
http://hdl.handle.net/2142/11702.
MLA Handbook (7th Edition):
Cai, Deng. “Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning.” 2009. Web. 20 Nov 2019.
Vancouver:
Cai D. Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2009. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/2142/11702.
Council of Science Editors:
Cai D. Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2009. Available from: http://hdl.handle.net/2142/11702
University of Illinois – Urbana-Champaign
17.
Jiang, Yiming.
Improvements and augmentations to Learning Based Java: a Java based learning based programming language.
Degree: MS, Computer Science, 2016, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/90827
► Machine Learning (ML) is the science that enables computers with the ability to learn without being explicitly programmed. ML is so pervasive today, with applications…
(more)
▼ Machine Learning (ML) is the science that enables computers with the ability to learn without being explicitly programmed. ML is so pervasive today, with applications in speech recognition, recommendation systems, fraud detection and many more that we may not be aware of. To facilitate a rapid pace of development, it is important to create a framework with modularity and reusability.
Learning Based Java (LBJava) was introduced by Cognitive Computation Group (CCG) to achieve such goal.
This thesis extends and introduces multiple components in LBJava. We begin by giving a comprehensive literature review relates to
Learning Based Programming (LBP) and LBJava.
Then we introduce regression evaluation metrics to LBJava. In addition, we introduce Adaptive Sub- Gradient (AdaGrad) for regression. Then we add a comprehensive tutorial with example on regression. Furthermore, we extend both SGD and AdaGrad algorithms for classification. Then we evaluate across var- ious
learning algorithms, with sparse and dense features, using large programmatically generated datasets.
Moreover, we introduce Neural Network (NN), in particular, Multilayer Perceptron (MLP), to LBJava. We also did some miscellaneous work.
Lastly, we conclude on all the extended and added components and provide recommendations for future work.
Advisors/Committee Members: Roth, Dan (advisor).
Subjects/Keywords: Machine Learning
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Manager
APA (6th Edition):
Jiang, Y. (2016). Improvements and augmentations to Learning Based Java: a Java based learning based programming language. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90827
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):
Jiang, Yiming. “Improvements and augmentations to Learning Based Java: a Java based learning based programming language.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed November 20, 2019.
http://hdl.handle.net/2142/90827.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Jiang, Yiming. “Improvements and augmentations to Learning Based Java: a Java based learning based programming language.” 2016. Web. 20 Nov 2019.
Vancouver:
Jiang Y. Improvements and augmentations to Learning Based Java: a Java based learning based programming language. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/2142/90827.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Jiang Y. Improvements and augmentations to Learning Based Java: a Java based learning based programming language. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90827
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
California State Polytechnic University – Pomona
18.
Alarifi, Hana.
Using Machine Learning to Predict Autism: Multiple Algorithms Comparison.
Degree: MS, Department of Computer Science, 2018, California State Polytechnic University – Pomona
URL: http://hdl.handle.net/10211.3/206664
► Machine learning is one of the most important current technologies. In many aspects of research, machine learning played an extensive role for development especially in…
(more)
▼ Machine learning is one of the most important current technologies. In many aspects of research,
machine learning played an extensive role for development especially in terms of data analytics. Healthcare is one of the fields where
Machine Learning made big phases of enhancements because of the huge amount of data being processed and analyzed. It changed the diagnosing process for better accuracy based on the new technologies emerged in the past decade that became tools in every doctor???s office and hospital. The purpose of this thesis is to research and implement multiple
machine learning techniques, to predict Autism Spectrum Disorder (ASD) over large scale datasets. Autism Spectrum Disorder is a developmental and neurological disorder. Autism is also identified as a range of conditions categorized by various challenges such as social skills, repetitive behaviors, Speech and non-verbal communication and unique strengths and differences. Autism signs can appear as early as 18 months, up to age three. The main cause of Autism is not defiantly identified; research shows links between genetics and the environmental factors with Autism. The ultimate goal of this thesis is to train the data model with the set of training data and then testing and evaluating the data model using the test data. As a result, the research will come up with a solution that applies
machine learning to detect autism.
Advisors/Committee Members: Young, Gilbert (advisor), Jayarathna, Sampath (committee member).
Subjects/Keywords: machine learning
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Manager
APA (6th Edition):
Alarifi, H. (2018). Using Machine Learning to Predict Autism: Multiple Algorithms Comparison. (Masters Thesis). California State Polytechnic University – Pomona. Retrieved from http://hdl.handle.net/10211.3/206664
Chicago Manual of Style (16th Edition):
Alarifi, Hana. “Using Machine Learning to Predict Autism: Multiple Algorithms Comparison.” 2018. Masters Thesis, California State Polytechnic University – Pomona. Accessed November 20, 2019.
http://hdl.handle.net/10211.3/206664.
MLA Handbook (7th Edition):
Alarifi, Hana. “Using Machine Learning to Predict Autism: Multiple Algorithms Comparison.” 2018. Web. 20 Nov 2019.
Vancouver:
Alarifi H. Using Machine Learning to Predict Autism: Multiple Algorithms Comparison. [Internet] [Masters thesis]. California State Polytechnic University – Pomona; 2018. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/10211.3/206664.
Council of Science Editors:
Alarifi H. Using Machine Learning to Predict Autism: Multiple Algorithms Comparison. [Masters Thesis]. California State Polytechnic University – Pomona; 2018. Available from: http://hdl.handle.net/10211.3/206664
California State Polytechnic University – Pomona
19.
Li, Wie Hsing.
Detecting Non-Credible News Using Machine Learning.
Degree: MS, Department of Computer Science, 2018, California State Polytechnic University – Pomona
URL: http://hdl.handle.net/10211.3/206666
► Many of us are connected to various social media outlets such as Facebook and Twitter. Social media is an online place where one could share…
(more)
▼ Many of us are connected to various social media outlets such as Facebook and Twitter. Social media is an online place where one could share interesting information, get updated about current events, and read people's reactions. However, ???click-baits???, satirical, false, or misleading articles can result in people making wrong judgements and decisions. The problem is how can people validate and judge information to be credible and maybe non bias before and after they are posted on social media.
The project involves using TF-IDF features from articles of credible/non-credible sources to train a model. The model is then validated by evaluating the accuracy of train and test set. The goal is to build a framework that detects non-credible sources and potentially filter them into groups consisting of if the source is trustworthy, or if it appears as though the story itself uses language that would indicate it is to be taken with an open mind. By using news sources and stories that is predetermined to be false, a model can be trained to look for similar language and predict if new articles follow these same patterns. The completion of this framework will lay the groundwork for developing a recommender system that could possibly suggest a balanced list of ???left and right??? articles based on the information you are reading.
Advisors/Committee Members: Young, Gilbert (advisor), Yu, Sun (committee member).
Subjects/Keywords: machine learning
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APA ·
Chicago ·
MLA ·
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CSE |
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to Zotero / EndNote / Reference
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APA (6th Edition):
Li, W. H. (2018). Detecting Non-Credible News Using Machine Learning. (Masters Thesis). California State Polytechnic University – Pomona. Retrieved from http://hdl.handle.net/10211.3/206666
Chicago Manual of Style (16th Edition):
Li, Wie Hsing. “Detecting Non-Credible News Using Machine Learning.” 2018. Masters Thesis, California State Polytechnic University – Pomona. Accessed November 20, 2019.
http://hdl.handle.net/10211.3/206666.
MLA Handbook (7th Edition):
Li, Wie Hsing. “Detecting Non-Credible News Using Machine Learning.” 2018. Web. 20 Nov 2019.
Vancouver:
Li WH. Detecting Non-Credible News Using Machine Learning. [Internet] [Masters thesis]. California State Polytechnic University – Pomona; 2018. [cited 2019 Nov 20].
Available from: http://hdl.handle.net/10211.3/206666.
Council of Science Editors:
Li WH. Detecting Non-Credible News Using Machine Learning. [Masters Thesis]. California State Polytechnic University – Pomona; 2018. Available from: http://hdl.handle.net/10211.3/206666
20.
Khorshidi, Alireza.
Methods and Theories in Atomistic Reaction
Engineering.
Degree: School of Engineering, 2018, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:792658/
► Electronic-structure calculations have provided us with in-depth understanding of chemical and physical phenomena in atomic and molecular scales. In particular, density functional theory has been…
(more)
▼ Electronic-structure calculations have provided us
with in-depth understanding of chemical and physical phenomena in
atomic and molecular scales. In particular, density functional
theory has been extensively used to complement experimental
observations, also to provide insights and mechanisms not
accessible by experiment in a wide range of scientific research
areas. There is however still a significant gap between the scale
of realistic experiments and the scale affordable by
quantum-mechanics approaches. This dissertation is devoted to two
possible approaches in order to narrow this gap. The first approach
is based on
machine learning, where quantum-mechanics potential
energy surface is learned by agnostic function approximators. The
function approximator can then be used for calculation of systems
having larger size in longer time-scale simulations, with typically
orders-of-magnitude faster speed as compared to quantum-mechanics
methods. In this dissertation we address several possible
frameworks for developing
machine-
learning potentials and force
fields. In particular, we discuss our Atomistic
Machine-
learning
Package (Amp) that we developed during the course of this thesis
preparation. Amp is an open-source package in python with fortran
counterparts to accelerate computationally-intensive calculations.
For the purpose of illustration, we then show how
machine-
learning
calculations inside Amp can accelerate stable cluster searches,
transition state searches, or can be coupled with quantum mechanics
in quantum-mechanics/
machine-
learning type of hybridization
schemes. As a second approach to save the number of required
quantum-mechanical calculations, we discuss possible theories
inspired from continuum mechanics that can qualitatively predict
the results of electronic-structure calculations, without the need
to do extra quantum mechanics. We illustrate such
continuum-mechanics approaches within the context of the effect of
mechanical strain on the energy of adsorption of gas-phase chemical
species on catalysts' surfaces. The methods and theories
discussed in this thesis exemplify possible approaches to narrow
the gap between quantum-mechanics scale and real experimental
scale, which seems to be of urgent need to be addressed for the
future progress in the science of physical chemistry.
Advisors/Committee Members: Peterson, Andrew A. (Advisor), Guduru, Pradeep R. (Reader), Rubenstein, Brenda M. (Reader).
Subjects/Keywords: Machine Learning
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Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Khorshidi, A. (2018). Methods and Theories in Atomistic Reaction
Engineering. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:792658/
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):
Khorshidi, Alireza. “Methods and Theories in Atomistic Reaction
Engineering.” 2018. Thesis, Brown University. Accessed November 20, 2019.
https://repository.library.brown.edu/studio/item/bdr:792658/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Khorshidi, Alireza. “Methods and Theories in Atomistic Reaction
Engineering.” 2018. Web. 20 Nov 2019.
Vancouver:
Khorshidi A. Methods and Theories in Atomistic Reaction
Engineering. [Internet] [Thesis]. Brown University; 2018. [cited 2019 Nov 20].
Available from: https://repository.library.brown.edu/studio/item/bdr:792658/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Khorshidi A. Methods and Theories in Atomistic Reaction
Engineering. [Thesis]. Brown University; 2018. Available from: https://repository.library.brown.edu/studio/item/bdr:792658/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
21.
Lee, Seungjoon.
Statistical Learning Tools for Information Fusion in
Computational Fluid Dynamics.
Degree: Department of Applied Mathematics, 2017, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:733405/
► For more than a decade, remarkable scientific progress in computational fluid dynamics (CFD) has been achieved via powerful collection of tools for exascale simulations, data-driven…
(more)
▼ For more than a decade, remarkable scientific progress
in computational fluid dynamics (CFD) has been achieved via
powerful collection of tools for exascale simulations, data-driven
approaches, and
machine-
learning (or statistical
learning)
techniques that enable efficient simulations of multiscale and
multiphysics problems. With the prospect of exascale simulations in
the next decade, it is clear that new flexible tools and
specialized algorithms are required to take advantage of such
unprecedented computing environment. For example, in the DOE ASCAC
subcommittee report, it is stated ``new algorithms will need to be
designed to optimize not only for floating-point performance and
accuracy, but also to minimize associated data movement, power, and
energy cost". Furthermore, robust and fault-resilient algorithms
are increasingly attracting attention in exascale simulations
against expected and repeated software or hardware error. Hence,
new algorithms for exascale simulations inherently require the
seamless integration of robustness, resilience, correctness, and
efficiency. Computational fluid dynamics has been developed
following different pathways to address for complex flow physics
problems. Many researches have focused on building up their own
model to describe and address multi-resolution and multi-physics
applications. Based on the recent introduction of data-driven
algorithms via
machine-
learning techniques in computer simulations,
computational fluid dynamics must rely also on data-driven
approaches as much as on the anticipated improvements in computer
hardware. In data science, data from various heterogeneous sources
can be used effectively to accelerate simulations via multiple
fidelity information fusion without additional complex models,
equations, and extra state variables. This could result in
establishing a new paradigm of multifidelity simulations in
computational fluid dynamics. In this thesis, we address these new
requirements for new algorithms in exascale simulations and
introduce a novel framework including fault-resilient, robust, and
efficient algorithms via multiple fidelity information fusion
realized via statistical
learning tools. This achievement addresses
the new capability that statistical
learning techniques can bring
to traditional scientific computing algorithms. This thesis
proposes two possible directions of a next generation of
computational frameworks for exascale simulations. The first
direction addresses resilience via information fusion with
auxiliary data. In exascale simulations, if each processor can
share some global information about the simulation from a coarse,
limited accuracy but relatively costless auxiliary simulator we can
effectively fill-in the missing spatial data at the required times
by a statistical
learning technique based on multi-level Gaussian
process regression, on-the-fly. The second direction addresses
efficiency via adaptive projective time integration. The
aforementioned auxiliary data provide additional information about
dynamics-informed…
Advisors/Committee Members: Karniadakis, George (Advisor), Kevrekidis, Ioannis (Advisor), Maxey, Martin (Reader), Perdikaris, Paris (Reader).
Subjects/Keywords: Machine Learning
Record Details
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Lee, S. (2017). Statistical Learning Tools for Information Fusion in
Computational Fluid Dynamics. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:733405/
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):
Lee, Seungjoon. “Statistical Learning Tools for Information Fusion in
Computational Fluid Dynamics.” 2017. Thesis, Brown University. Accessed November 20, 2019.
https://repository.library.brown.edu/studio/item/bdr:733405/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lee, Seungjoon. “Statistical Learning Tools for Information Fusion in
Computational Fluid Dynamics.” 2017. Web. 20 Nov 2019.
Vancouver:
Lee S. Statistical Learning Tools for Information Fusion in
Computational Fluid Dynamics. [Internet] [Thesis]. Brown University; 2017. [cited 2019 Nov 20].
Available from: https://repository.library.brown.edu/studio/item/bdr:733405/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lee S. Statistical Learning Tools for Information Fusion in
Computational Fluid Dynamics. [Thesis]. Brown University; 2017. Available from: https://repository.library.brown.edu/studio/item/bdr:733405/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
22.
Brawner, Stephen Andrew.
Algorithms for the Personalization of AI for Robots and the
Smart Home.
Degree: Department of Computer Science, 2018, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:792905/
► Just as an interconnected-computerized world has produced large amounts of data resulting in exciting challenges for machine learning, connected households with robots and smart devices…
(more)
▼ Just as an interconnected-computerized world has
produced large amounts of data resulting in exciting challenges for
machine learning, connected households with robots and smart
devices will provide developers with an opportunity to build
technologies that learn from personalized household data. However,
there exists a dilemma. When limited data is available for a user,
for example when they initially procure a new smart device or
robot, there will be a substantial burden placed on that user to
personalize it to their household by the learner. At the outset,
applying predictions learned from a general population to a user
will provide better predictive success. But as the amount of data
provided by the user increases, intelligent methods should choose
predictions more heavily weighted by the individuals examples. This
work investigated three problems to find algorithms that learn from
both the general population and specialize to the human individual.
We developed a solution to reduce the interactive burden when
telling a robot how to organize a kitchen by applying a
context-aware recommender system. Also, using the paradigm of
trigger-action programming made popular by IFTTT, we sought to
improve the programming experience by
learning to predict the
creation of programs from the user's history. Finally we developed
several methods to personalize grounding natural language to these
trigger-action programs. In a smart home where a user can describe
to an intelligent home automated system rules or programs they
desire to be created, their utterances are highly context
dependent. Multiple users may use similar utterances to mean
different things. We present several methods that personalize the
machine translation of these utterances to smart home programs.
This work presents several problems that show that
learning
algorithms that learn from both a general population and from
personalized interactions will perform better than either
learning
approach alone.
Advisors/Committee Members: Charniak, Eugene (Reader), Littman, Michael L. (Advisor), Cakmak, Maya (Reader).
Subjects/Keywords: Machine Learning
Record Details
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Brawner, S. A. (2018). Algorithms for the Personalization of AI for Robots and the
Smart Home. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:792905/
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):
Brawner, Stephen Andrew. “Algorithms for the Personalization of AI for Robots and the
Smart Home.” 2018. Thesis, Brown University. Accessed November 20, 2019.
https://repository.library.brown.edu/studio/item/bdr:792905/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Brawner, Stephen Andrew. “Algorithms for the Personalization of AI for Robots and the
Smart Home.” 2018. Web. 20 Nov 2019.
Vancouver:
Brawner SA. Algorithms for the Personalization of AI for Robots and the
Smart Home. [Internet] [Thesis]. Brown University; 2018. [cited 2019 Nov 20].
Available from: https://repository.library.brown.edu/studio/item/bdr:792905/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Brawner SA. Algorithms for the Personalization of AI for Robots and the
Smart Home. [Thesis]. Brown University; 2018. Available from: https://repository.library.brown.edu/studio/item/bdr:792905/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
23.
Wang, Yinong.
Decoding hindlimb kinematics from primate motor cortex using
long short-term memory recurrent neural networks.
Degree: Biomedical Engineering, 2018, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:792720/
► Recent machine learning techniques have become a powerful tool in a variety of tasks, including neural decoding. Artificial neural network models, particularly recurrent models, can…
(more)
▼ Recent
machine learning techniques have become a
powerful tool in a variety of tasks, including neural decoding.
Artificial neural network models, particularly recurrent models,
can leverage the temporal evolution of neural ensemble activity to
decode complex motor and sensory signals. Using single-unit
recordings from microelectrode arrays implanted in the leg area of
primary motor cortex in non-human primates, we decoded the
positions and angles of hindlimb joints during a locomotion task
using a long short-term memory (LSTM) network. The LSTM decoder
significantly improved decoding over traditional filtering methods,
such as linear filtering techniques. However, marginal improvements
over other
machine learning and latent state-space methods were
observed.
Advisors/Committee Members: Borton, David (Advisor), Truccolo, Wilson (Advisor), Silverman, Harvey (Reader).
Subjects/Keywords: Machine Learning
Record Details
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Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Wang, Y. (2018). Decoding hindlimb kinematics from primate motor cortex using
long short-term memory recurrent neural networks. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:792720/
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, Yinong. “Decoding hindlimb kinematics from primate motor cortex using
long short-term memory recurrent neural networks.” 2018. Thesis, Brown University. Accessed November 20, 2019.
https://repository.library.brown.edu/studio/item/bdr:792720/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wang, Yinong. “Decoding hindlimb kinematics from primate motor cortex using
long short-term memory recurrent neural networks.” 2018. Web. 20 Nov 2019.
Vancouver:
Wang Y. Decoding hindlimb kinematics from primate motor cortex using
long short-term memory recurrent neural networks. [Internet] [Thesis]. Brown University; 2018. [cited 2019 Nov 20].
Available from: https://repository.library.brown.edu/studio/item/bdr:792720/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wang Y. Decoding hindlimb kinematics from primate motor cortex using
long short-term memory recurrent neural networks. [Thesis]. Brown University; 2018. Available from: https://repository.library.brown.edu/studio/item/bdr:792720/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
24.
Patil, Prerna.
Multi-Fidelity Simulation Algorithm and its Application to
Flow over a Cylinder.
Degree: School of Engineering, 2017, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:733471/
► We tackle the classical problem of predicting the relation between of C_L , C_D and C_P vs Reynolds number for flow over cylinder using the…
(more)
▼ We tackle the classical problem of predicting the
relation between of C_L , C_D and C_P vs Reynolds number for flow
over cylinder using the multi-fidelity framework. The stochastic
response surface is obtained by implementing the auto-regressive
stochastic modeling (Kennedy and O’Hagan, 2000) and Gaussian
process regression to combine data from variable levels of
fidelity. In particular, we predict the lift, drag and pressure
coefficients where codes with multiple levels of fidelity are
available. We correlate data at each of these levels and build the
surrogate model using co-kriging technique. The deficient physics
of the low-fidelity model is explored by examining the
cross-correlation between the low-fidelity and high-fidelity
models. The proposed framework ultimately intends to meld
computational accuracy of the expensive high fidelity with the
computational cost of the inexpensive low-fidelity.
Advisors/Committee Members: Karniadakis, George (Advisor).
Subjects/Keywords: Machine Learning
Record Details
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Patil, P. (2017). Multi-Fidelity Simulation Algorithm and its Application to
Flow over a Cylinder. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:733471/
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):
Patil, Prerna. “Multi-Fidelity Simulation Algorithm and its Application to
Flow over a Cylinder.” 2017. Thesis, Brown University. Accessed November 20, 2019.
https://repository.library.brown.edu/studio/item/bdr:733471/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Patil, Prerna. “Multi-Fidelity Simulation Algorithm and its Application to
Flow over a Cylinder.” 2017. Web. 20 Nov 2019.
Vancouver:
Patil P. Multi-Fidelity Simulation Algorithm and its Application to
Flow over a Cylinder. [Internet] [Thesis]. Brown University; 2017. [cited 2019 Nov 20].
Available from: https://repository.library.brown.edu/studio/item/bdr:733471/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Patil P. Multi-Fidelity Simulation Algorithm and its Application to
Flow over a Cylinder. [Thesis]. Brown University; 2017. Available from: https://repository.library.brown.edu/studio/item/bdr:733471/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
25.
Loper, Jackson Hoy.
Theory and Computation for Modern Probabilistic
Models.
Degree: Department of Applied Mathematics, 2017, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:733424/
► Modern probabilistic models involve computation and analysis in very high-dimensional spaces. Here we explore several of ways in which analysis of problems high dimensional spaces…
(more)
▼ Modern probabilistic models involve computation and
analysis in very high-dimensional spaces. Here we explore several
of ways in which analysis of problems high dimensional spaces can
be made more tractable by various reductions. In particular, we
focus on finite approximations for sampling point processes,
particle methods for sampling high-dimensional distributions,
situations in which hitting times for brownian motion in high
dimensional space take on particularly simple forms, and certain
maximal characteristics on the infinite-dimensional space of
couplings of two random variables with fixed marginal
distributions.
Advisors/Committee Members: Geman, Stuart (Advisor), Ramanan, Kavita (Reader), Harrison, Matthew (Reader).
Subjects/Keywords: Machine Learning
Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Loper, J. H. (2017). Theory and Computation for Modern Probabilistic
Models. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:733424/
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):
Loper, Jackson Hoy. “Theory and Computation for Modern Probabilistic
Models.” 2017. Thesis, Brown University. Accessed November 20, 2019.
https://repository.library.brown.edu/studio/item/bdr:733424/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Loper, Jackson Hoy. “Theory and Computation for Modern Probabilistic
Models.” 2017. Web. 20 Nov 2019.
Vancouver:
Loper JH. Theory and Computation for Modern Probabilistic
Models. [Internet] [Thesis]. Brown University; 2017. [cited 2019 Nov 20].
Available from: https://repository.library.brown.edu/studio/item/bdr:733424/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Loper JH. Theory and Computation for Modern Probabilistic
Models. [Thesis]. Brown University; 2017. Available from: https://repository.library.brown.edu/studio/item/bdr:733424/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
26.
Chua, Jeroen.
Probabilistic Scene Grammars: A General-Purpose Framework
For Scene Understanding.
Degree: Department of Computer Science, 2017, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:792615/
► We propose a general-purpose probabilistic framework for scene understanding tasks. We show that several classical scene understanding tasks can be modeled and addressed under a…
(more)
▼ We propose a general-purpose probabilistic framework
for scene understanding tasks. We show that several classical scene
understanding tasks can be modeled and addressed under a common
representation, approximate inference scheme, and
learning
algorithm. We refer to this approach as the Probabilistic Scene
Grammar (PSG) framework. The PSG framework models scenes using
probabilistic grammars which capture relationships between objects
in terms of compositional rules that provide important contextual
cues for inference with ambiguous data. We show how to represent
the distribution defined by a probabilistic grammar using a factor
graph. We also show how to estimate the parameters of a grammar
using an approximate version of Expectation-Maximization, and
describe an approximate inference scheme using Loopy Belief
Propagation with an efficient message-passing scheme. Inference
with Loopy Belief Propagation naturally combines bottom-up and
top-down contextual information and leads to a robust algorithm for
aggregating evidence. To demonstrate the generality of the
approach, we evaluate the PSG framework on the scene understanding
tasks of contour detection, face localization, and binary image
segmentation. The results of the PSG framework are competitive with
algorithms specialized for these scene understanding
tasks.
Advisors/Committee Members: Felzenszwalb, Pedro (Advisor), Geman, Stuart (Reader), Sudderth, Erik (Reader).
Subjects/Keywords: Machine Learning
Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Chua, J. (2017). Probabilistic Scene Grammars: A General-Purpose Framework
For Scene Understanding. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:792615/
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):
Chua, Jeroen. “Probabilistic Scene Grammars: A General-Purpose Framework
For Scene Understanding.” 2017. Thesis, Brown University. Accessed November 20, 2019.
https://repository.library.brown.edu/studio/item/bdr:792615/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Chua, Jeroen. “Probabilistic Scene Grammars: A General-Purpose Framework
For Scene Understanding.” 2017. Web. 20 Nov 2019.
Vancouver:
Chua J. Probabilistic Scene Grammars: A General-Purpose Framework
For Scene Understanding. [Internet] [Thesis]. Brown University; 2017. [cited 2019 Nov 20].
Available from: https://repository.library.brown.edu/studio/item/bdr:792615/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Chua J. Probabilistic Scene Grammars: A General-Purpose Framework
For Scene Understanding. [Thesis]. Brown University; 2017. Available from: https://repository.library.brown.edu/studio/item/bdr:792615/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
27.
Ren, Zhile.
Semantic Three-Dimensional Understanding of Dynamic
Scenes.
Degree: Department of Computer Science, 2018, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:792891/
► We develop new representations and algorithms for three-dimensional (3D) scene understanding from images and videos. To model cluttered indoor scenes, we introduce object descriptors that…
(more)
▼ We develop new representations and algorithms for
three-dimensional (3D) scene understanding from images and videos.
To model cluttered indoor scenes, we introduce object descriptors
that account for camera viewpoint, and use structured
learning to
perform 3D object detection and room layout prediction. We further
boost accuracy by using latent support surfaces to capture style
variations of objects and help detect small objects. In outdoor
environments, we incorporate semantic segmentation in a cascaded
prediction framework to more accurately model the 3D scene flow of
moving objects. We first propose a cloud of oriented gradient (COG)
descriptor that links the 2D appearance and 3D pose of object
categories, and thus accurately models how perspective projection
affects perceived image boundaries. We also propose a Manhattan
voxel representation which better captures room layout geometry.
Effective classification rules are learned via a structured
prediction framework. Contextual relationships among categories and
layout are captured via a cascade of classifiers. Furthermore, we
design algorithms that use latent support surfaces to better
represent the 3D appearance of large objects, and provide
contextual cues to improve the detection of small objects. Our
model is learned solely from annotated RGB-D images, but
nevertheless its performance substantially exceeds the
state-of-the-art on the SUN RGB-D database. We then focus on
outdoor scene flow prediction. Many existing approaches use
superpixels for regularization. We instead assume that scenes
consist of foreground objects rigidly moving in front of a static
background, and use semantic cues to produce pixel-accurate scene
flow estimates. Our cascaded classification framework accurately
models scenes by iteratively refining semantic segmentation masks,
stereo correspondences, 3D rigid motion estimates, and optical flow
fields. Our method has state-of-the-art performance on the
challenging KITTI autonomous driving benchmark.
Advisors/Committee Members: Sudderth, Erik (Advisor), Felzenszwalb, Pedro (Reader), Tompkin, James (Reader).
Subjects/Keywords: Machine Learning
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
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to Zotero / EndNote / Reference
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APA (6th Edition):
Ren, Z. (2018). Semantic Three-Dimensional Understanding of Dynamic
Scenes. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:792891/
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):
Ren, Zhile. “Semantic Three-Dimensional Understanding of Dynamic
Scenes.” 2018. Thesis, Brown University. Accessed November 20, 2019.
https://repository.library.brown.edu/studio/item/bdr:792891/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ren, Zhile. “Semantic Three-Dimensional Understanding of Dynamic
Scenes.” 2018. Web. 20 Nov 2019.
Vancouver:
Ren Z. Semantic Three-Dimensional Understanding of Dynamic
Scenes. [Internet] [Thesis]. Brown University; 2018. [cited 2019 Nov 20].
Available from: https://repository.library.brown.edu/studio/item/bdr:792891/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ren Z. Semantic Three-Dimensional Understanding of Dynamic
Scenes. [Thesis]. Brown University; 2018. Available from: https://repository.library.brown.edu/studio/item/bdr:792891/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
28.
Tang, Yu-Hang.
Multiscale and Mesoscopic Modeling of Soft Matter and
Biophysical Systems Using High Performance Computing and Machine
Learning.
Degree: Department of Applied Mathematics, 2017, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:792611/
► This dissertation is composed around the subject of multiscale modeling of soft matter and biophysical systems with applications using large-scale computations. Specifically, it is expanded…
(more)
▼ This dissertation is composed around the
subject of
multiscale modeling of soft matter and biophysical systems with
applications using large-scale computations. Specifically, it is
expanded on three fronts: 1) Development of high-performance
simulators and computational frameworks. On this front, I will
discuss the design of three sets of software. The first one is an
accelerated parallel particle simulator, which features many
algorithmic innovations for harnessing the massively parallel
threading architecture of general purpose graphics processing
units. The second one is an ultrafast coarse-grained molecular
dynamics simulator, which enables the simulation of an entire human
red blood cell at protein resolution using a single computer
workstation. This is realized by a novel algorithm that allows
neighbor search in a sparse 3D space in linear time.The third one
is a generic framework that utilizes the concept of meshless
interpolation to faciliate the implementation of parallel
concurrently coupled multiscale simulations. 2) Construction of
mesoscopic models for amphiphilic and thermo-responsive polymers
and their applications to large-scale mesoscopic simulations. This
is manifested in a detailed study of the non-equilibrium dynamics
of thermo-responsive polymers. One of the most interesting findings
is that a thermo-responsive polymer membrane may invert its layered
structure without actually rotating any of its composing molecules.
3) Data-driven algorithms for
learning complex interatomic force
fields. Here I have focus on a specific aspect of this field, i.e.
the design of feature vectors that can efficiently and accurately
quantify the similarity between atomistic configurations. To
achieve this, a kernel minisum approach is proposed as a robust and
efficient replacement of the principal component analysis
algorithm. A set of quadrature rules and parameters are also
proposed for constructing a smoothed density field that allows
either inner product- or norm-based comparison of structural
similarity.
Advisors/Committee Members: Karniadakis, George (Advisor), Maxey, Martin (Reader), Baker, Nathan (Reader).
Subjects/Keywords: Machine Learning
Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Tang, Y. (2017). Multiscale and Mesoscopic Modeling of Soft Matter and
Biophysical Systems Using High Performance Computing and Machine
Learning. (Thesis). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:792611/
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):
Tang, Yu-Hang. “Multiscale and Mesoscopic Modeling of Soft Matter and
Biophysical Systems Using High Performance Computing and Machine
Learning.” 2017. Thesis, Brown University. Accessed November 20, 2019.
https://repository.library.brown.edu/studio/item/bdr:792611/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Tang, Yu-Hang. “Multiscale and Mesoscopic Modeling of Soft Matter and
Biophysical Systems Using High Performance Computing and Machine
Learning.” 2017. Web. 20 Nov 2019.
Vancouver:
Tang Y. Multiscale and Mesoscopic Modeling of Soft Matter and
Biophysical Systems Using High Performance Computing and Machine
Learning. [Internet] [Thesis]. Brown University; 2017. [cited 2019 Nov 20].
Available from: https://repository.library.brown.edu/studio/item/bdr:792611/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Tang Y. Multiscale and Mesoscopic Modeling of Soft Matter and
Biophysical Systems Using High Performance Computing and Machine
Learning. [Thesis]. Brown University; 2017. Available from: https://repository.library.brown.edu/studio/item/bdr:792611/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
29.
Masci, Jonathan.
Advances in deep learning for vision, with applications to
industrial inspection: classification, segmentation and
morphological extensions.
Degree: 2014, Università della Svizzera italiana
URL: http://doc.rero.ch/record/210177
► Learning features for object detection and recognition with deep learning has received increasing attention in the past several years and recently attained widespread popularity. In…
(more)
▼ Learning features for object detection and recognition
with deep
learning has received increasing attention in the past
several years and recently attained widespread popularity. In this
PhD thesis we investigate its applications to the automatic surface
inspection system of our industrial partner ArcelorMittal, for
classification and segmentation problems. Currently employed
algorithms, in fact, use fixed feature extractors which are hard to
tune and require extensive prior-knowledge. Our work, instead,
focuses on learnable systems that can be used to improve
recognition and detection without requiring hard to obtain
task-specific domain knowledge. For image classification we propose
extensions to max-pooling convolutional networks, so that they can
be applied to solve the general defect classification problem via a
new pooling and feature encoding schemes. State-of-the-art deep
learning algorithms for object detection/segmentation have reached
outstanding performance given high-quality annotated data.
Unfortunately, they do not meet the required processing speeds of
steel industry. We propose an architecture that does not suffer the
same computational bottleneck (1500-fold speed-up) while retaining
equal performance. To further advance the field we study the
learning of morphological operators, largely used in industry. Only
few attempts have been proposed in the literature, but no approach
has ever considered the problem in its generality because of its
hard formulation. We tackle it from a different perspective and
introduce a learnable framework which seamlessly integrates
morphological operators; hence bringing these powerful tools to
deep
learning for the first time. Re-engineering an industrial
system requires time. In order to deliver an immediate return we
investigate metric
learning problems to boost performance of
currently used features. Our multimodal similarity sensitive
hashing model scales well to web-scale datasets and, thanks to the
binary representation, requires little storage and involves a cheap
distance computation. It outperforms previous state-of-the-art
approaches without requiring additional resources.
Advisors/Committee Members: Schmidhuber, Jürgen (Dir.).
Subjects/Keywords: Machine learning
Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Masci, J. (2014). Advances in deep learning for vision, with applications to
industrial inspection: classification, segmentation and
morphological extensions. (Thesis). Università della Svizzera italiana. Retrieved from http://doc.rero.ch/record/210177
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):
Masci, Jonathan. “Advances in deep learning for vision, with applications to
industrial inspection: classification, segmentation and
morphological extensions.” 2014. Thesis, Università della Svizzera italiana. Accessed November 20, 2019.
http://doc.rero.ch/record/210177.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Masci, Jonathan. “Advances in deep learning for vision, with applications to
industrial inspection: classification, segmentation and
morphological extensions.” 2014. Web. 20 Nov 2019.
Vancouver:
Masci J. Advances in deep learning for vision, with applications to
industrial inspection: classification, segmentation and
morphological extensions. [Internet] [Thesis]. Università della Svizzera italiana; 2014. [cited 2019 Nov 20].
Available from: http://doc.rero.ch/record/210177.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Masci J. Advances in deep learning for vision, with applications to
industrial inspection: classification, segmentation and
morphological extensions. [Thesis]. Università della Svizzera italiana; 2014. Available from: http://doc.rero.ch/record/210177
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Università della Svizzera italiana
30.
Bacchelli, Alberto.
Mining unstructured software data.
Degree: 2013, Università della Svizzera italiana
URL: http://doc.rero.ch/record/203066
► Our thesis is that the analysis of unstructured data supports software understanding and evolution analysis, and complements the data mined from structured sources. To this…
(more)
▼ Our thesis is that the analysis of unstructured data
supports software understanding and evolution analysis, and
complements the data mined from structured sources. To this aim, we
implemented the necessary toolset and investigated methods for
exploring, exposing, and exploiting unstructured data.To validate
our thesis, we focused on development email data. We found two main
challenges in using it to support program comprehension and
software development: The disconnection between emails and code
artifacts and the noisy and mixed-language nature of email content.
We tackle these challenges proposing novel approaches. First, we
devise lightweight techniques for linking email data to code
artifacts. We use these techniques for creating a tool to support
program comprehension with email data, and to create a new set of
email based metrics to improve existing defect prediction
approaches. Subsequently, we devise techniques for giving a
structure to the content of email and we use this structure to
conduct novel software analyses to support program comprehension.
In this dissertation we show that unstructured data, in the form of
development emails, is a valuable addition to structured data and,
if correctly mined, can be used successfully to support software
engineering activities.
Advisors/Committee Members: Michele (Dir.).
Subjects/Keywords: Machine learning
Record Details
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Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bacchelli, A. (2013). Mining unstructured software data. (Thesis). Università della Svizzera italiana. Retrieved from http://doc.rero.ch/record/203066
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):
Bacchelli, Alberto. “Mining unstructured software data.” 2013. Thesis, Università della Svizzera italiana. Accessed November 20, 2019.
http://doc.rero.ch/record/203066.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Bacchelli, Alberto. “Mining unstructured software data.” 2013. Web. 20 Nov 2019.
Vancouver:
Bacchelli A. Mining unstructured software data. [Internet] [Thesis]. Università della Svizzera italiana; 2013. [cited 2019 Nov 20].
Available from: http://doc.rero.ch/record/203066.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Bacchelli A. Mining unstructured software data. [Thesis]. Università della Svizzera italiana; 2013. Available from: http://doc.rero.ch/record/203066
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
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Open Access Theses and Dissertations
