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Description
Title: | Using information theoretic measures to evaluate support vector machine kernels |
Author(s): | Pierce, Austin |
Advisor(s): | Blahut, Richard E. |
Department / Program: | Electrical & Computer Eng |
Discipline: | Electrical & Computer Engr |
Degree Granting Institution: | University of Illinois at Urbana-Champaign |
Degree: | M.S. |
Genre: | Thesis |
Subject(s): | Renyi
Support Vector Machine (SVM) Renyi Entropy |
Abstract: | A new method is proposed that exploits the underlying information theoretic structure in the input data to evaluate the ability of a kernel to successfully separate a class in some feature space. This method is built on the fundamental idea that kernel density estimation in some input space is equivalent to an inner product on some Hilbert space. Estimators of Renyi's generalized form of information theoretic measurements reduce to a form that gives an elegant characterization of the geometric properties of the kernel in the feature space. It is shown how these estimators can be used to evaluate the kernel of a support vector machine. |
Issue Date: | 2012-05-22 |
URI: | http://hdl.handle.net/2142/30953 |
Rights Information: | Copyright 2012 Austin Pierce |
Date Available in IDEALS: | 2012-05-22 |
Date Deposited: | 2012-05 |
This item appears in the following Collection(s)
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Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois -
Dissertations and Theses - Electrical and Computer Engineering
Dissertations and Theses in Electrical and Computer Engineering