支持向量机的新发展

Vapnik等学者首先提出了实现统计学习理论中结构风险最小化原则的实用算法一支持向量机，比较成功地解决了模式分类问题，其后，机器学习界兴起了研究统计学习理论和支持向量机的热湖，引人瞩目的研究分支有从最优化技术出发改进或改造支持向量机，依据统计学习理论和支持向量机的优点设计新的非线性机器学习算法等，对此，较为系统地回顾了近lO年来算法研究领域的新发展。

Advances in support vector machines

Vapnik and his collaborators proposed a useful algorithm: support vector machines, which can implement the structural risk minimization principle in statistical learning theory. This novel algorithm handles the classification problems successfully. Since then more attentions have been paid to statistical learning theory and support vector machines. The attractive research areas are to improve or modify support vector machines by optimization techniques, and to design the novel non-linear machine learning algorithms based on statistical learning theory and some ideas in support vector machines, etc. The advances in such algorithm studies in the last ten years are reviewed.