Kernel methods: a survey of current techniques

Kernel methods have become an increasingly popular tool for machine learning tasks such as classification, regression or novelty detection. They exhibit good generalization performance on many real-life datasets, there are few free parameters to adjust and the architecture of the learning machine does not need to be found by experimentation. In this tutorial, we survey this subject with a principal focus on the most well-known models based on kernel substitution, namely, support vector machines. (C) 2002 Elsevier Science B.V. All rights reserved.