CAJ | 학술논문

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、研究现状和一些思考。
통계학습이론시유Vapnik등인제출적일충유한양본통계이론，시모식식별영역신근발전적일충신이론，착중연구재소양본정황하적통계규률급학습방법성질。타위소양본궤기학습문제건립료일개교호적이론광가，야발전료일충신적통용학습산법——지지향량궤，교호지해결료소양본궤기학습문제。해문지재개소통계학습이론적기본사상、특점、연구현상화일사사고。
Statistical Learning Theory,a recently developed new theory forpattern 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.