关键词: 支持向量机, 机器学习, 文本分类, 分类模型, KKT条件

Abstract: With information constantly updating and sample collecting, the classification performance and accuracy of initial training model using I-SVM is of low efficiency and costs long time. To solve this problem, this paper describes a growing Incremental Supported Vector Machine algorithm（I-SVM） based on support vector threshold control optimization. The TI-SVM algorithm removes most of the non-support vector which aims at new sample sets and the historical sample set that are based on historical training model and the KKT conditions pretreatment. According to the sample after the pretreatment set, this algorithm trains a new SVM model. It takes vantage of the similarity of the text and the default threshold of support vector system to give a further treatment to the redundancy of support vector and to improve the classification performance. The theoretical analysis and experimental results show that the algorithm is effective with a high classification accuracy.

Key words: Support Vector Machine（SVM）, machine learning, text classification, model of classification, Karush-Kuhn-Tucker（KKT）