SoftDoubleMinOver: a simple procedure for maximum margin classification

Authors:
Thomas Martinetz;Kai Labusch;Daniel Schneegaß
Affiliations:
Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany;Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany;Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

The well-known MinOver algorithm is a simple modification of the perceptron algorithm and provides the maximum margin classifier without a bias in linearly separable two class classification problems. DoubleMinOver as a slight modification of MinOver is introduced, which now includes a bias. It is shown how this simple and iterative procedure can be extended to SoftDoubleMinOver for classification with soft margins and with kernels. On benchmarks the extremely simple SoftDoubleMinOver algorithm achieves the same classification performance with the same computational effort as sophisticated Support-Vector-Machine software.