Support-Vector Networks

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Support-Vector Networks (1995)

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by Corinna Cortes , Vladimir Vapnik

Venue:	Machine Learning
Citations:	3701 - 35 self

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@INPROCEEDINGS{Cortes95support-vectornetworks,
    author = {Corinna Cortes and Vladimir Vapnik},
    title = {Support-Vector Networks},
    booktitle = {Machine Learning},
    year = {1995},
    pages = {273--297}
}

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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 supportvector 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.

Keyphrases

support-vector network feature space training data following idea input vector new learning machine decision surface linear decision surface learning machine restricted case supportvector network special property high generalization ability two-group classification problem non-separable training data high-dimension feature space non-linearly mapped

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