关键词: 非凸在线支持向量机, 支持向量机, 特征项, 词频, 相关频率, 文本分类

Abstract: Non-convex online support vector machine（LASVM-NC） has the advantages of strong anti-noise ability and fast training speed, while term frequency relevance frequency?product （tf.rf） is a very good text feature for adaptive classification performance. LASVM-NC+tf.rf is proposed as a new text classification method by combining non-convex support vector machines with term frequency relevance frequency product. It has been shown that the method can perform better than LASVM-NC plus many other features. Moreover, the method can produce faster trained and more robust classifiers with greater generalization and sparser expression than SVM+tf.rf in processing noisy and large-scale datasets.

Key words: non-convex online support vector machine, support vector machines, term weighting, term frequency, relevance frequency, text classification