Hierarchical Learning in Polynomial Support Vector Machines

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,...

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Bibliographic Details
Published in:Machine learning 2002-01, Vol.46 (1-3), p.53-70
Main Authors: Risau-gusman, Sebastian, Gordon, Mirta B
Format: Article
Language:English
Subjects:
Artificial intelligence
Computer programming
Computer science
Condensed Matter
Other
Physics
Polynomials
Online Access:Get full text
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Description
Summary: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.[PUBLICATION ABSTRACT]
ISSN:0885-6125
1573-0565
DOI:10.1023/A:1012442008732