关键词: 贪婪支持向量机, 推广性能, 稀疏学习

Abstract: It is a crucial issue to analyze the generalization performance of support vector machines in the field of machine learning. In this paper, following the idea that support vector machine can be constructed by minimizing the number of the essential support vectors, from the view of greedy method, a new kind of support vector machine named greedy support vector machine is presented combined with the problem of sparse learning. Applying the breast cancer data in UCI database, the performance of this kind of support vector machine on making a balance between estimation accuracy and sparsity of the solution is tested. Moreover for the greedy support vector machine designed, the generalization performance of this kind of support vector machine is derived in terms of the technique in empirical process.

Key words: greedy support vector machine, generalization performance, sparse learning