摘要
作者针对单个核函数构成的SVM并不能满足诸如数据异构或不规则、样本规模巨大、样本分布不平坦等实际应用的需求,而将多个核函数进行组合,以获得更好的效果,提出一种基于多核的模糊支持向量机算法。此算法决策树中的模糊核权重主要是借助于样本各自的模糊因子来确定。仿真实验数据表明:与传统单核函数支持向量机相比,多核模糊支持向量机具有很好的优越性。
In order to solve those question which a kernel function can not meet, such as heterogeneous or irregmar ,data, largc sample size, uneven distribution of the sample the acttlal application requirements, and obtain better results, the author of this paper proposes a support vector machine algorithm based on multi-core fuzzy. Fuzzy kernel weights of this decision tree algo- rithm is mainly determined by the fuzzy factors of the sample. The simulation data show that multi-core fuzzy support vector machine has the good the superiority compared with the traditional single-kernel function support vector machine.
出处
《重庆师范大学学报(自然科学版)》
CAS
北大核心
2012年第6期50-53,共4页
Journal of Chongqing Normal University:Natural Science
基金
重庆市教委科技计划项目(No.KJ090823)
重庆师范大学博士研究基金(No.11XLB047)
关键词
多核
模糊集
模糊支持向量机
多核分类算法
multi-core
fuzzy set
fuzzy support vector machine
multi-core classification algorithm