Informational Energy Kernel for LVQ

Document Type

Article

Department or Administrative Unit

Computer Science

Publication Date

9-11-2005

Abstract

We describe a kernel method which uses the maximization of Onicescu’s informational energy as a criteria for computing the relevances of input features. This adaptive relevance determination is used in combination with the neural-gas and the generalized relevance LVQ algorithms. Our quadratic optimization function, as an L 2 type method, leads to linear gradient and thus easier computation. We obtain an approximation formula similar to the mutual information based method, but in a more simple way.

Comments

This article was originally published in Lecture Notes in Computer Science. The full-text article from the publisher can be found here.

Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.

Journal

Lecture Notes in Computer Science

Rights

© Springer-Verlag Berlin Heidelberg 2005

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