The Support Vector method
The Support Vector (SV) method is a new general method of function estimation which does not depend explicitly on the dimensionality of input space. It was applied for pattern recognition, regression estimation, and density estimation problems as well as for problems of solving linear operator equations. In this article we describe the general idea of the SV method and present theorems demonstrating that the generalization ability of the SV method is based on factors which classical statistics do not take into account. We also describe the SV method for density estimation in a set of functions defined by a mixture of an infinite number of Gaussians.
KeywordsSupport Vector Generalization Ability Pattern Recognition Problem Optimal Hyperplane Kernel Representation
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