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Hierarchical learning in polynomial Support Vector Machines

Sebastian Risau-Gusman·Mirta B. Gordon
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Abstract

We study the typical properties of polynomial Support Vector Machines within a Statistical Mechanics approach that allows us to analyze the effect of different normalizations of the features. If the normalization is adecuately chosen, there is a hierarchical learning of features of increasing order as a function of the training set size.

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Sebastian Risau-Gusman, Mirta B. Gordon. 2000-10-26. Hierarchical learning in polynomial Support Vector Machines. https://arxiv.org/abs/cond-mat/0010423

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