摘要
支持向量机在高维度、小样本情况下具有独特优势,但同时支持向量机的参数优化极大制约了其分类效果,目前参数优化缺乏系统的理论指导;针对传统DAG-SVM训练分类器较多,训练耗时长,分类效果受到结构排序的影响,提出了一种基于"1 vs R"策略的改进型算法;针对SVM传统参数优化方式耗时大,优化精度不高,提出了改进型人工鱼群算法;最后结合1 vs R-DAG支持向量机算法与改进型人工鱼群算法,得到一种新的改进型支持向量机算法;仿真对比实验证实,对支持向量机的参数优化是有效可行的。
Support vector machine（SVM） algorithm has much more advantages than other classify algorithm under high-dimensional,small sample and multi-class situation.But at the same time,the parameters optimization has been one of the main factors restricting SVM effect and there is no clear theory to guide it.For original DAG-SVM algorithm＇ s long time cost and randomness,an improved algorithm has been proposed;For traditional SVM parameter optimization＇ s large time consuming and unsatisfactory results,an improved artificial fish swarm algorithm has been proposed;Finally,an improved support vector machine algorithm combined IvsR-DAG-SVM and IAFSA has been proposed.Simulation experiments confirm that the SVM parameter optimization proposed in this paper is feasible and effective.
出处
《计算机测量与控制》
2016年第5期237-241,共5页
Computer Measurement &Control
基金
国家自然科学基金项目(61403265)
关键词
支持向量机
人工鱼群算法
参数优化
有向无环图
support vector machine
artificial fish swarm algorithm
parameter optimization
directed acyclic graph
