Support-Vector Networks

Authors: Cortes C.¹; Vapnik V.²

Source: Machine Learning, Volume 20, Number 3, September 1995 , pp. 273-297(25)

Publisher: Springer

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Abstract:

The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.

High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

Keywords: pattern recognition; efficient learning algorithms; neural networks; radial basis function classifiers; polynomial classifiers

Language: English

Document Type: Research article

Affiliations: 1: AT&T Bell Labs., Holmdel, NJ 07733, USA. corinna@neural.att.com 2: AT&T Bell Labs., Holmdel, NJ 07733, USA. vlad@neural.att.com

Publication date: 1995-09-01