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| a novel granular support vector machine based on mixed kernel function | |
| Huang Huajuan; Ding Shifei; Jin Fengxiang; Yu Junzhao; Han Youzhen | |
| 2012 | |
| Source | International Journal of Digital Content Technology and its Applications
 |
| ISSN | 1975-9339 |
| Volume | 6Issue:20Pages:484-492 |
| English Abstract | 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. |
| Indexed Type | EI |
| Keyword | Algorithms Granulation Particles (Particulate Matter) |
| Department | (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 |
| Language | 英语 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15456 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation 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. |
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