摘要
介绍了一种新的支持向量机(SVM),其优化问题的对偶问题为具有简单界约束的凸二次规划问题;探讨了将活跃集迭代法运用于这种SVM的学习算法以及初始活跃集的选取问题;针对增量学习和大规模学习问题,提出了基于活跃集迭代法的SVM快速增量学习算法;实验验证了算法的有效性。
A new support vector machine (SVM) was introduced. What is unusual for the SVM is that the dual problem for the constrained optimization of the SVM is a convex quadratic problem with simple bound constraints. The active set iteration method for this quadratic problem was applied as a learning algorithm for the SVM, and the selection of the initial active set was discussed. For incremental learning and large-scale learning problems, a fast incremental learning algorithm for the SVM was proposed. Some experiments show the efficiency of the proposed algorithm.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2006年第11期3305-3308,3312,共5页
Journal of System Simulation
基金
国家自然科学基金项目(No.60375010
No.60572128)
安徽大学人才队伍建设项目
创新团队基金资助。
关键词
支持向量机
增量学习
活跃集迭代法
凸二次规划
support vector machines
incremental learning
active set iterations
convex quadratic optimization