Abstract
The recently proposed twin support vector machines, denoted by TWSVM, gets perfect classification performance and is suitable for many cases. However, it would reduce its learning performance when it is used to solve the large number of samples. In order to solve this problem, a novel algorithm called Granular Twin Support Vector Machines based on Mixture Kernel Function (GTWSVM-MK) is proposed. Firstly, a grain method including coarse particles and fine particles is propsed and then the judgment and extraction methods of support vector particles are given. On the above basis, we propose a granular twin support vector machine learning model. Secondly, in order to solve the kernel function selection problem, minxture kernel function is introduced. Finally, compared with SVM and TWSVM, the experimental results show that GTWSVM-MK has higher classification performance.
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Acknowledgment
This work is supported by the Foundation of Guangxi University of science and technology research project (2013YB326), and the Fundation of Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis (No. HCIC201304).
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Wei, X., Huang, H. (2015). Granular Twin Support Vector Machines Based on Mixture Kernel Function. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_5
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DOI: https://doi.org/10.1007/978-3-319-22053-6_5
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