Hierarchical Learning in Polynomial Support Vector Machines View Full Text


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Article Info

DATE

2002-01

AUTHORS

Sebastian Risau-Gusman, Mirta B. Gordon

ABSTRACT

We study the typical properties of polynomial Support Vector Machines within a Statistical Mechanics approach that takes into account the number of high order features relative to the input space dimension. We analyze the effect of different features' normalizations on the generalization error, for different kinds of learning tasks. If the normalization is adequately selected, hierarchical learning of features of increasing order takes place as a function of the training set size. Otherwise, the performance worsens, and there is no hierarchical learning at all. More... »

PAGES

53-70

References to SciGraph publications

  • 1995-09. Support-vector networks in MACHINE LEARNING
  • 1996. Statistical Mechanics of Generalization in MODELS OF NEURAL NETWORKS III
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1023/a:1012442008732

    DOI

    http://dx.doi.org/10.1023/a:1012442008732

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1001181535


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