关于统计学习理论与支持向量机

模式识别、函数拟合及概率密度估计等都属于基于数据学习的问题,现有方法的重 要基础是传统的统计学,前提是有足够多样本,当样本数目有限时难以取得理想的效果.统计 学习理论(SLT)是由Vapnik等人提出的一种小样本统计理论,着重研究在小样本情况下的 统计规律及学习方法性质.SLT为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(SVM),能够较好的解决小样本学习问题.目前,SLT和 SVM已成为国际上机器学习领域新的研究热点.本文是一篇综述,旨在介绍SLT和SVM的 基本思想、特点和研究发展现状,以引起国内学者的进一步关注.

INTRODUCTION TO STATISTICAL LEARNING THEORY AND SUPPORT VECTOR MACHINES

Data-based machine learning covers a wide range of topics from pattern recognition to function regression and density estimation. Most of the existing methods are based on traditional statistics, which provides conclusion only for the situation where sample size is tending to infinity. So they may not work in practical cases of limited samples. Statistical Learning Theory or SLT is a small-sample statistics by Vapnik et al. , which concerns mainly the statistic principles when samples are limited, especially the properties of learning procedure in such cases. SLT provides us a new framework for the general learning problem, and a novel powerful learning method called Support Vector Machine or SVM, which can solve small-sample learning problems better. It is believed that the study of SLT and SVM is becoming a new hot area in the field of machine learning. This review introduces the basic ideas of SLT and SVM, their major characteristics and some current research trends.