Support-Vector Networks View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1995-09

AUTHORS

Corinna Cortes, Vladimir Vapnik

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. More... »

PAGES

273-297

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1022627411411

DOI

http://dx.doi.org/10.1023/a:1022627411411

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005207849


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