A Data Complexity Approach to Kernel Selection for Support Vector Machines

We describe a data complexity approach to kernel selection based on the behavior of polynomial and Gaussian kernels. Our results show how the use of a Gaussian kernel produces a gram matrix with useful local information that has no equivalent counterpart in polynomial kernels. By exploiting neighborhood information embedded by data complexity measures, we are able to carry out a form of meta-generalization. Our goal is to predict which data sets are more favorable to particular kernels (Gaussian or polynomial). The end result is a framework to improve the model selection process in Support Vector Machines.