MaxMinOver regression: a simple incremental approach for support vector function approximation

Authors:
Daniel Schneegaß;Kai Labusch;Thomas Martinetz
Affiliations:
Institute for Neuro- and Bioinformatics, University at Lübeck, Lübeck, Germany;Institute for Neuro- and Bioinformatics, University at Lübeck, Lübeck, Germany;Institute for Neuro- and Bioinformatics, University at Lübeck, Lübeck, Germany
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

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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. In [1] and [2] we presented DoubleMinOver and MaxMinOver as extensions of MinOver which provide the maximal margin solution in the primal and the Support Vector solution in the dual formulation by dememorising non Support Vectors. These two approaches were augmented to soft margins based on the ν-SVM and the C2-SVM. We extended the last approach to SoftDoubleMaxMinOver [3] and finally this method leads to a Support Vector regression algorithm which is as efficient and its implementation as simple as the C2-SoftDoubleMaxMinOver classification algorithm.