Support-vector networks

作者:Corinna Cortes, Vladimir Vapnik

摘要

Thesupport-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.

论文关键词:pattern recognition, efficient learning algorithms, neural networks, radial basis function classifiers, polynomial classifiers

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论文官网地址:https://doi.org/10.1007/BF00994018