Hierarchical learning in polynomial Support Vector Machines
  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. 
Sebastian Risau-Gusman and Mirta B. Gordon