a novel granular support vector machine based on mixed kernel function

ISCAS OpenIR

	a novel granular support vector machine based on mixed kernel function
	Huang Huajuan; Ding Shifei; Jin Fengxiang; Yu Junzhao; Han Youzhen
	2012
发表期刊	International Journal of Digital Content Technology and its Applications
ISSN	1975-9339
卷号	6 期号:20 页码:484-492
摘要	The constaints of time and memory will reduce the learning performance of Support Vector Machine (SVM) when it is used to solve the large number of samples. In order to solve this problem, a novel algorithm called Granular Support Vector Machine based on Mixed Kernel Function (GSVM-MKF) is proposed. Firstly, the granular method is propsed and then the judgment and extraction methods of support vector particles are given. On the above basis, we propose a new granular support vector machine learning model. Secondly, in order to further improve the performance of the granular support vector machine learning model, a mixed kernel function which effectively uses the global kernel function having the good generalization ability and the local kernel function having good learning ability is proposed. Finally, the theoretical analysis and experimental results show the effectiveness of the method.; The constaints of time and memory will reduce the learning performance of Support Vector Machine (SVM) when it is used to solve the large number of samples. In order to solve this problem, a novel algorithm called Granular Support Vector Machine based on Mixed Kernel Function (GSVM-MKF) is proposed. Firstly, the granular method is propsed and then the judgment and extraction methods of support vector particles are given. On the above basis, we propose a new granular support vector machine learning model. Secondly, in order to further improve the performance of the granular support vector machine learning model, a mixed kernel function which effectively uses the global kernel function having the good generalization ability and the local kernel function having good learning ability is proposed. Finally, the theoretical analysis and experimental results show the effectiveness of the method.
收录类别	EI
关键词	Algorithms Granulation Particles (Particulate Matter)
部门归属	(1) School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China; (2) Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China; (3) Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China; (4) Geomatics College Shandong University of Science and Technology Qingdao 266510 China
语种	英语
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15456
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Huang Huajuan,Ding Shifei,Jin Fengxiang,et al. a novel granular support vector machine based on mixed kernel function[J]. International Journal of Digital Content Technology and its Applications,2012,6(20):484-492.
APA	Huang Huajuan,Ding Shifei,Jin Fengxiang,Yu Junzhao,&Han Youzhen.(2012).a novel granular support vector machine based on mixed kernel function.International Journal of Digital Content Technology and its Applications,6(20),484-492.
MLA	Huang Huajuan,et al."a novel granular support vector machine based on mixed kernel function".International Journal of Digital Content Technology and its Applications 6.20(2012):484-492.