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Title:Learning in High Dimensional Spaces: Applications, Theory, and Algorithms
Author(s):Ashutosh
Doctoral Committee Chair(s):Huang, Thomas S.; Roth, Dan
Department / Program:Electrical Engineering
Discipline:Electrical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Artificial Intelligence
Abstract:The theoretical results are used to extend the existing learning algorithms. Based on the results from probabilistic classifiers, we have proposed an improved learning algorithm for HMMs which attempts to learn a maximum likelihood classifier under the minimum conditional entropy prior. A margin distribution optimization algorithm is proposed based on the results on generalization bounds and our results show that this new algorithm is better than the existing SVM and boosting algorithms.
Issue Date:2003
Type:Text
Language:English
Description:110 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2003.
URI:http://hdl.handle.net/2142/80814
Other Identifier(s):(MiAaPQ)AAI3086006
Date Available in IDEALS:2015-09-25
Date Deposited:2003


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