关键词: 半监督分类, 支持向量机, 离散度量

Abstract: Semi-supervised classification algorithm attempts to establish a set of recognition methods and criteria for specific unknown samples based on known samples.PTSVM（Progressive Transductive Support Vector Machine） is a semi-supervised classification algorithm based on SVM.In this paper，a semi-supervised classification algorithm based on the combination of the separation degree and support vector machine is devised，which uses the separation degree in Fisher criteria as metric and Fisher criteria as evaluation function.The experimental results show that this algorithm achieves a better learning result than PTSVM on time complexity and sample test precision.

Key words: semi-supervised classification, support vector machine, separation degree