Support-Vector Networks |
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Authors: | Cortes Corinna Vapnik Vladimir |
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Affiliation: | (1) AT&T Bell Labs., 07733 Holmdel, NJ, USA |
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Abstract: | 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.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. |
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Keywords: | pattern recognition efficient learning algorithms neural networks radial basis function classifiers polynomial classifiers |
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