The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Self averaging and the space of interactions in neural networks
Random Structures & Algorithms
Bounds on Error Expectation for Support Vector Machines
Neural Computation
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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.