支持向量机惩罚参数的自适应调整方法

Adaptive adjust method for penalization parameter of support vector machines

训练样本集中异常样本的存在会使得支持向量机分类超平面过度复杂,降低了分类器的分类效率和泛化性能,在分析这种问题产生原因的基础之上,提出了一种支持向量机惩罚参数的自适应调整方法。实验证明,该方法简单易行且具有更好的抗干扰能力及更高的推广性能,在工程实际中有着较好的应用前景。

The extreme sample in training sample set usually make the separation hyper-surface of support vector machines unnecessarily over-convoluted,thus affecting both the classification efficiency and the generalization ability of classifier.After analyzing the reason for this problem,an adaptive adjust method for penalization parameter of support vector machines is proposed.The experimental result shows that this method not only is simple and feasible but also has better anti-jamming ability and higher generalization ability.And it will have a better application foreground in practical work.