Aggregation of SVM Classifiers Using Sobolev Spaces
Sébastien Loustau.
Year: 2008, Volume: 9, Issue: 50, Pages: 1559−1582
Abstract
This paper investigates statistical performances of Support Vector Machines (SVM) and considers the problem of adaptation to the margin parameter and to complexity. In particular we provide a classifier with no tuning parameter. It is a combination of SVM classifiers.
Our contribution is two-fold: (1) we propose learning rates for SVM using Sobolev spaces and build a numerically realizable aggregate that converges with same rate; (2) we present practical experiments of this method of aggregation for SVM using both Sobolev spaces and Gaussian kernels.