A novel learning model-Kernel Granular Support Vector Machine

Author

Guo, Hu-sheng ; Wang, Wen-jian ; Men, Chang-qian

Author_Institution

Sch. of Comput. & Inf. Technol., Shanxi Univ., Taiyuan, China

Volume

2

fYear

2009

fDate

12-15 July 2009

Firstpage

930

Lastpage

935

Abstract

This paper presents a novel machine learning model-kernel granular support vector machine (KGSVM), which combines traditional support vector machine (SVM) with granular computing theory. By dividing granules and replacing with them in kernel space, the datasets can be reduced effectively without changing data distribution. And then the generalization performance and training efficiency of SVM can be improved. Simulation results on UCI datasets demonstrate that KGSVM is highly scalable for large datasets and very effective in terms of classification.

Keywords

learning (artificial intelligence); pattern classification; support vector machines; UCI datasets; data distribution; granular computing theory; learning model-kernel granular support vector machine; machine learning model; pattern classification; Cybernetics; Kernel; Machine learning; Machine learning algorithms; Mathematical model; Pattern recognition; Quadratic programming; Support vector machine classification; Support vector machines; Training data; Granules; Kernel granular support vector machine; Kernel space; Support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2009 International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3702-3

Electronic_ISBN

978-1-4244-3703-0

Type

conf

DOI

10.1109/ICMLC.2009.5212413

Filename

5212413