摘要
为了克服支持向量机(SVM)中单核函数的局限性,经常使用混合核函数做预测,但混合核函数中各函数权重难以确定.为解决该问题,提出了一种基于特征距离的权重求解方法.该方法首先利用支持向量机的几何意义,根据同类样本特征距离最小化和异类样本特征距离最大化原理,分析得出优化函数,然后对优化函数求解得出权重系数.实验结果表明,与传统的交叉验证法和PSO算法相比,该方法在保证预测精度的情况下,将计算时间减少了70%左右.
In order to overcome the limitation of single kernel in Support Vector Machine（SVM） model, hybrid kernel is usually used in forecasting. However, the weight of functions in the hybrid kernel is hard to calculate. To solve this problem, we propose a new method based on feature.distance. This method firstly gets an optimization function based on SVM＇s geometric meaning and a principle, which is the feature-distance of the same kind should be minimized and the different should bc maximized, and then analyzes the optimization function to work out the weight. Experimental results show that compared with the cross validation method and PSO algorithm, this method reduces the computing time nearly by 70% with the accuracy kept unchanged.
出处
《计算机系统应用》
2015年第4期129-133,共5页
Computer Systems & Applications
基金
国家科技重大专项(2012ZX10004-301-609)
国家自然科学基金(61272472
61232018
61202404)
安徽省教学研究计划2010
