A novel learning model-Kernel Granular Support Vector Machine

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 cha...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 2; pp. 930 - 935
Main Authors Hu-Sheng Guo, Wen-Jian Wang, Chang-Qian Men
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Cybernetics
Granules
Kernel
Kernel granular support vector machine
Kernel space
Machine learning
Machine learning algorithms
Mathematical model
Pattern recognition
Quadratic programming
Support vector machine
Support vector machine classification
Support vector machines
Training data
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Summary: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.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212413