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《Journal of Guangxi Normal University(Natural Science Edition)》 2009-03
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A Kernel Granular Support Vector Machine Based on Association Rules

ZHANG Wen-hao,WANG Wen-jian(Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,College of Computer and Information Technology,Shanxi University,Taiyuan 030006,China)  
This paper proposes a kernel granular support vector machine based on association rules(AR-KGSVM)learning approach.Firstly,samples in input space are mapped into high-dimensional feature space and then association rules based on distance metric are mined in kernel future space.These association rules are applied to divide granules.Both the granular dividing and data training are executed in the high-dimensional kernel space.Hence,the proposed AR-KGSVM can avoid the poor generalization performance like traditional granular SVM(GSVM) due to the inconsistent data distribution,where granules are divided in the low-dimensional space but samples are trained in high-dimensional space.The experimental results on benchmark datasets demonstrate that the performance of AR-KGSVM is superior to that of traditional SVM and GSVM.
【Fund】: 国家自然科学基金资助项目(60673095);; 国家863高技术研究发展计划资助项目(2007AA01Z165);; 教育部新世纪优秀人才支持计划资助项目(NCET-07-0525);; 教育部科学技术研究重点项目(208021);; 山西省高校青年学术带头人基金资助项目;; 山西省留学归国人员基金资助项目(2008-14);; 山西省自然科学基金重点项目(2009011017-2)
【CateGory Index】: TP181
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