Improving support vector machine classifiers by modifying kernel functions

Шун-ичи Амари

doi:10.1016/S0893-6080(99)00032-5

Return

Improving support vector machine classifiers by modifying kernel functions

1999-07-01

803

PRE

Шун-ичи Амари *

吴

吴思 (Si Wu)

DOI：10.1016/S0893-6080(99)00032-5

Original

Original request for help

Save

Abstract

En 中文

We propose a method of modifying a kernel function to improve the performance of a support vector machine classifier. This is based on the structure of the Riemannian geometry induced by the kernel function. The idea is to enlarge the spatial resolution around the separating boundary surface, by a conformal mapping, such that the separability between classes is increased. Examples are given specifically for modifying Gaussian Radial Basis Function kernels. Simulation results for both artificial and real data show remarkable improvement of generalization errors, supporting our idea. (C) 1999 Elsevier Science Ltd. All rights reserved.

Keywords:

support vector machine

pattern classification

information geometry

kernel function

radial basis function

Riemannian geometry

kernel adatron

nonlinear classification

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.