Support-Vector Networks

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.

Summary¶

Introduces support-vector networks for nonseparable two-class classification, extending maximum-margin ideas through nonlinear feature maps and slack for errors. The paper is the canonical source for soft-margin SVMs and helped establish kernel methods as a practical, high-generalization learning framework.

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