关键词: 统计学习理论, 粒计算, 商空间, 支持向量机（SVM）

Abstract: In Internet information age, how to operate objective data mining based on complex date remains a central problem in many fields. The major approach to the problem is machine learning based on statistical learning theory. Granular computing has a wide range of applications in data mining. This paper proposes a method to get a better granular computing and statistical learning method by combining statistical learning theory and granular computing. Finally, this paper gives a statistical classification algorithm based on granular computing, and compares the results with support vector machine (SVM) and covering algorithm. The experimental results show that the classification effect of granulized support vector is better.

Key words: statistical learning theory, granular computing, quotient space, support vector machine (SVM)