Hierarchical learning in polynomial Support Vector Machines

RISAU GUSMAN, SEBASTIAN; GORDON, MIRTA B.

Machine Learning

Kluwer

Lugar: Amsterdam; Año: 2002 vol. 46 p. 53 - 70

0885-6125

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.