关键词: 单类支持向量机, 随机选择, 支持向量机分类, 大型数据集

Abstract: Support Vector Machine（SVM） is one of the most effective classifiers，which has very high classification accuracy and good generalization ability.However，SVM training yet is very complicated for large-scale data sets.A one-class SVM（OC-SVM）-based classification is proposed.In this method，training sets are reduced by the random selection algorithm to increase training speed.Meanwhile，original data which are the neighbors of samples in hypersphere intersection are recovered to ensure SVM classification accuracy.The experimental results show that the method reduces the computational complexity at a great extent，so the training speed is improved for large-scale data sets.

Key words: One-Class Support Vector Machine（OC-SVM）, random selection, Support Vector Machine（SVM） classification, large-scale data sets