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MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation

  • Daniel Schneegaß
  • Kai Labusch
  • Thomas Martinetz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)

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.

Keywords

Support Vector Machine Support Vector Input Vector Support Vector Regression Step Width 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniel Schneegaß
    • 1
    • 2
  • Kai Labusch
    • 1
  • Thomas Martinetz
    • 1
  1. 1.Institute for Neuro- and BioinformaticsUniversity at LübeckLübeckGermany
  2. 2.Information & Communications, Learning SystemsSiemens AG, Corporate TechnologyMunichGermany

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