Kernel methods: a survey of current techniques

摘要: Abstract 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 does not need be found by experimentation. In this tutorial, we survey subject with a principal focus most well-known models based kernel substitution, namely, support vector machines.