Learning in High Dimensional Spaces: Applications, Theory, and Algorithms
Ashutosh
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Permalink
https://hdl.handle.net/2142/80814
Description
Title
Learning in High Dimensional Spaces: Applications, Theory, and Algorithms
Author(s)
Ashutosh
Issue Date
2003
Doctoral Committee Chair(s)
Huang, Thomas S.
Roth, Dan
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Artificial Intelligence
Language
eng
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.
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