基于Seeded-Kmeans和SVM的分类算法

A Classification Algorithm Based on Seeded-Kmeans and SVM

支持向量机(support vector machines,SVM)在人像识别、文本分类等模式识别问题中有广泛的应用,可以有效地解决一些实际生活中的分类问题。针对半监督两分类问题,提出了基于Seeded-Kmeans和SVM的分类算法(SK-SVM)。用SeededKmeans算法对无标签点进行处理,使其获得初始标签,再选取有效的标签点加入已有带标签点中,构成新的带标签训练集,最后结合SVM进行分类。选取UCI中的8个数据集进行数值实验,基于Seeded-Kmeans和SVM的分类算法的有效性得到了验证。

Support vector machine is widely used in pattern recognition problems such as the portrait recognition and the text classification recognition.It can effectively solve some classification problems in real life.In this paper,a classifica⁃tion algorithm based on Seeded-Kmeans and SVM(SK-SVM)is proposed for the semi-supervised two classification problem.The Seeded-Kmeans algorithm is used to process the unlabeled points to obtain initial labels.Then,effective la⁃bel points are selected and added to the existing labeled points to form a new labeled training set.Finally,SVM is com⁃bined to classify the unlabeled points.