计算机工程与应用Issue(14)：144-146,3.DOI:10.3778/j.issn.1002-8331.1301-0195
基于k均值聚类的直推式支持向量机学习算法
k means clustering based transductive support vector machine algorithm.
摘要
Abstract
As transductive support vector machine runs slowly, this paper proposes ak means clustering based transductive sup-port vector machine algorithm. The algorithm utilizes k means clustering to divide the unlabeled samples into several clusters, labels them with the same class, makes transductive inference on the mixed data set composed by both labeled and unlabeled samples. As TSVMKMC algorithm reduces the size of the state space effectively, the running speed is improved largely. The experimental results show that the algorithm can achieve good classification accuracy with faster speed.关键词
直推式学习/支持向量机/k均值聚类/无标签样本Key words
transductive inference/support vector machine/k means clustering/unlabeled samples分类
信息技术与安全科学引用本文复制引用
王立梅,李金凤,岳琪..基于k均值聚类的直推式支持向量机学习算法[J].计算机工程与应用,2013,(14):144-146,3.基金项目
黑龙江省自然科学基金（No.F200919）；牡丹江市科技局攻关项目（No.G2011a852）；牡丹江师范学院基金项目（No.QZ201212）。 （）
