Improving Support Vector Classification via the Combination of Multiple Sources of Information
Lecture notes in computer sciencePublished 1 January 2004
Javier M. Moguerza, Alberto Muñoz, Isaac Martín de Diego
Citations29
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This paper describes several new methods to build a kernel matrix from a collection of kernels that will be used for classification purposes using Support Vector Machines (SVMs).
Abstract
In this paper we describe several new methods to build a kernel matrix from a collection of kernels. This kernel will be used for classification purposes using Support Vector Machines (SVMs). The key idea is to extend the concept of linear combination of kernels to the concept of functional (matrix) combination of kernels. The functions involved in the combination take advantage of class conditional probabilities and nearest neighbour techniques. The proposed methods have been successfully evaluated on a variety of real data sets against a battery of powerful classifiers and other kernel combination techniques.
Keywords
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