文件名称：Kernel methods:a survey of current techniques
文件大小：176KB
文件格式：PDF
更新时间：2014-04-28 04:12:40
SVM
Kernel methods have become an increasingly popular tool for machine learning tasks such as classi+cation, 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.