Statistical Mechanics of Support Vector Networks

Details Statistical Mechanics of Support Vector Networks Statistical Mechanics of Support Vector Networks

1998-11-30

arxiv.org/abs/cond-mat/9811421v2

Physics

Condensed Matter

Disordered Systems and Neural Networks

REVTeX, 4 pages, 2 figures, accepted by Phys. Rev. Lett (typos corrected)

Scientific paper

10.1103/PhysRevLett.82.2975

Using methods of Statistical Physics, we investigate the generalization performance of support vector machines (SVMs), which have been recently introduced as a general alternative to neural networks. For nonlinear classification rules, the generalization error saturates on a plateau, when the number of examples is too small to properly estimate the coefficients of the nonlinear part. When trained on simple rules, we find that SVMs overfit only weakly. The performance of SVMs is strongly enhanced, when the distribution of the inputs has a gap in feature space.

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