模糊分类系统的邻域原理设计算法

Design algorithm of fuzzy classification system based on neighborhood theory

使用支持向量机算法直接求海量数据的模糊分类系统是相当困难的。为了解决这个问题,提出了基于邻域原理设计模糊分类系统的方法。将支持向量机的理论建立在距离空间上,设计出了计算支持向量的邻域算法;利用所求的支持向量,基于平分最近点方法设计出了求分类超平面的算法,求出模糊分类系统,该算法优于基于支持向量机直接求模糊分类系统的方法。实验结果说明,该方法可有效地解决对海量数据的模糊分类系统的设计问题。

It is rather difficult to design a fuzzy massive data classification system by using support vector machine directly algorithm. To solve this problem, a method is proposed to design fuzzy classification system based on neighborhood theory. Vapnik＇s support vector machine theory is constructed on the distance space, the algorithm is designed to compute support vectors; Make use of the support vector, the algorithm is designed to calculate optimal classification hyper plane based on the method of dividing equally point recent, then, to get fuzzy classification system, that calculate way better than the method of getting directly fuzzy classification system according to support the vector machine; The result of experiment illustrates the problem of designing fuzzy massive data classification system is effectively solved by using this method.