局部支持向量机的研究进展

摘要

Support Vector Machine(SVM) is an important and widely used classifier. If a sample wants to be classified, all training data will be used to obtain a hyperplane which is used to determine the label of that sample, that is, the SVM worked in global manner. However,this global behaviour doesn't imply consistency. The design of Local SVM(LSVM) is in accordance with the result of"consistency implies local behaviour". In this paper,we first reviewed the main idea of LSVM, followed by the improvements on LSVM. In the following, we presented an LSVM algorithm which is based on cooperative clustering,reducing the time complexity of LSVM in large scaled dataset. Then we ended this article by the conclusion.%支持向量机是一种用途广泛的分类器,标准的支持向量机在预测每个样本点的类别时使用了训练集中所有的样本信息(即全局信息),然而这种全局化的方法并不蕴含一致性.局部支持向量机的提出符合“一致性蕴含局部性”的思路.首先回顾局部支持向量机的主要思想,然后阐述各种关于局部支持向量机的改进,并提出基于协同聚类的局部支持向量机用于大规模数据集,最后对局部支持向量机进行总结.