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Alan S. Willsky. Varshney, Kush R. (Kush Raj) Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2010-12-06T17:34:43Z 2010-12-06T17:34:43Z 2010 2010 http://hdl.handle.net/1721.1/60182 681911430 Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. Cataloged from PDF version of thesis. Includes bibliographical references (p. 157-175). The design and analysis of decision rules using detection theory and statistical learning theory is important because decision making under uncertainty is pervasive. Three perspectives on limiting the complexity of decision rules are considered in this thesis: geometric regularization, dimensionality reduction, and quantization or clustering. Controlling complexity often reduces resource usage in decision making and improves generalization when learning decision rules from noisy samples. A new margin-based classifier with decision boundary surface area regularization and optimization via variational level set methods is developed. This novel classifier is termed the geometric level set (GLS) classifier. A method for joint dimensionality reduction and margin-based classification with optimization on the Stiefel manifold is developed. This dimensionality reduction approach is extended for information fusion in sensor networks. A new distortion is proposed for the quantization or clustering of prior probabilities appearing in the thresholds of likelihood ratio tests. This distortion is given the name mean Bayes risk error (MBRE). The quantization framework is extended to model human decision making and discrimination in segregated populations. by Kush R. Varshney. Ph.D. 175 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Frugal hypothesis testing and classification Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f0896f3d-8bb7-4f0e-a591-90e4ab35d209"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Frugal hypothesis testing and classification</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2010</PublicationDate> <Authors> <Author> <DisplayName>Varshney, Kush R. (Kush Raj)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>The design and analysis of decision rules using detection theory and statistical learning theory is important because decision making under uncertainty is pervasive. Three perspectives on limiting the complexity of decision rules are considered in this thesis: geometric regularization, dimensionality reduction, and quantization or clustering. Controlling complexity often reduces resource usage in decision making and improves generalization when learning decision rules from noisy samples. A new margin-based classifier with decision boundary surface area regularization and optimization via variational level set methods is developed. This novel classifier is termed the geometric level set (GLS) classifier. A method for joint dimensionality reduction and margin-based classification with optimization on the Stiefel manifold is developed. This dimensionality reduction approach is extended for information fusion in sensor networks. A new distortion is proposed for the quantization or clustering of prior probabilities appearing in the thresholds of likelihood ratio tests. This distortion is given the name mean Bayes risk error (MBRE). The quantization framework is extended to model human decision making and discrimination in segregated populations.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>