小样本机器学习理论:统计学习理论

Small-sample Machine Learning Theory: Statistical Learning Theory

统计学习理论是由Vapnik等人提出的一种有限样本统计理论 ,是模式识别领域新近发展的一种新理论 ,着重研究在小样本情况下的统计规律及学习方法性质。它为小样本机器学习问题建立了一个较好的理论框架 ,也发展了一种新的通用学习算法———支持向量机 ,较好地解决了小样本机器学习问题。该文旨在介绍统计学习理论的基本思想、特点。

Statistical Learning Theory,a recently developed new theory for pattern recognition,is a small sample statistics proposed by Vapnik et al,which deals mainly with the statistic principles when samples are limited,especially to describe the properties of learning procedure in such cases.It provides us a new framework for the small sample learning problem,and also a novel powerful learning method called Support Vector Machine,which can solve small sample learning problem better.This paper will introduce the basic ideas of the theory,its major characteristics,some current research trends of it and some thinking from us about it.