Aggregation of SVM classifiers using Sobolev spaces

作者：Loustau Sebastien^*

来源：Journal of Machine Learning Research, 2008, 9: 1559-1582.

• classification
• Support Vector Machines
• learning rates
• approximation
• aggregation of classifiers

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.

• 出版日期2008-7