孪生支持向量机综述

孪生支持向量机(Twin Support Vector Machine,TWSVM)是在支持向量机(Support Vector Machine,SVM)的基础上发展而来的一种新的机器学习方法。作为一种二分类的分类器,其基本思想为寻找两个超平面,使得每一个分类面靠近本类样本点而远离另一类样本点。作为一种新兴的机器学习方法,孪生支持向量机自提出以来便引起了国内外学者的广泛关注,已经成为机器学习领域的研究热点。对孪生支持向量机的最新研究进展进行综述,首先介绍了孪生支持向量机的基本概念与基本模型；然后对近几年来新型的孪生支持向量机模型与研究进展进行了总结,并对其代表算法进行了优缺点分析和实验比较；最后对将来的研究工作进行了展望。

Twin Support Vector Machine:A Review

Twin support vector machine (TWSVM) is a useful extension of the traditional support vector machine(SVM).For the binary classification problem,the basic idea of TWSVM is to seek two nonparallel hyperplanes such that each hyperplane is closer to one and is at least one distance from the other.As an emerging machine learning me-thod,TWSVM has attracted the attention of scholars and become a hotspot in machine learnig.This paper reviewed the development of TWSVM.At first,this paper analyzed the basic concept of the twin support vector machine,summarized the models and research process of novel algorithms of TWSVM in the last several years.Then,it analyzed the advantages and disadvantages of them and performed experiments on them.At last,it prospected the research work of TWSVM.