一种组合核支持向量机建模的方案

Approach of combined-kernel support vector machines

针对组合核支持向量机建模中存在的耗时和性能的矛盾问题,提出新的方案,用于在时间和性能上寻找理想折衷。研究了兼顾学习和推广能力的核组合,以及优化核参数方法。提出了一种主从核逐步优化的方案,即每次只优化一个核的核参数,逐步加入其他子核求解参数,时间上大致是求解单核参数耗时的简单叠加,相对于进化算法求解模型耗时更少,相对于分治算法求解模型性能更优。提出的方案在时间和性能上取得了较好的效果。

For the contradiction between time and performance in the modeling of combined-kernel support vector machines, it proposes a new scheme in finding the ideal compromise of performance and time.Kernels’combination is studied beneficial for learning and generalization ability,as well as the method of optimizing kernels’parameters.A master-slave kernel phase-optimized programs is proposed,namely a time optimizes parameters of one kernel,and gradually adds other sub-kernel. Time cost is the simple sum of time every single-kernel modeling consumed.Compared with the evolutionary algorithm,time consumed is less,and compared with the partition algorithm,the performance is better.The proposed scheme in terms of time and performance achieves good results.