Using information theoretic measures to evaluate support vector machine kernels

Pierce, Austin

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https://hdl.handle.net/2142/30953 Copy

Description

Title

Using information theoretic measures to evaluate support vector machine kernels

Author(s)

Pierce, Austin

Issue Date

2012-05-22T00:18:12Z

Director of Research (if dissertation) or Advisor (if thesis)

Blahut, Richard E.

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(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.

Graduation Semester

2012-05

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http://hdl.handle.net/2142/30953

Copyright and License Information

Copyright 2012 Austin Pierce

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