基于活跃集迭代法的支持向量机快速增量学习算法

Fast Incremental SVM Learning Algorithm Based on Active Set Iterations

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.