Abstract
Support vector machine (SVM) has been often used in binary classification. In order to seek the guidance principles of the kernel function selection, this paper analyzed a variety of kernel functions used to construct the SVM classifiers and carried out comparative studies on the 4 data sets for binary classification of UCI Machine Learning Repository. The experimental results show that, using the nu-SVC with radial basis kernel function (RBF) has the optimal classification accuracy, but using the C-SVC with RBF kernel function has the best generalization ability.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant No. 61003129 and the Planned Science and Technology Project of Shanxi Province, China, under Grant No. 2010JM8039 and also supported by the Fundamental Research Funds for the Central Universities of China under Grant No. GK201302055.
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Bao, Y., Wang, T., Qiu, G. (2014). Research on Applicability of SVM Kernel Functions Used in Binary Classification. In: Patnaik, S., Li, X. (eds) Proceedings of International Conference on Computer Science and Information Technology. Advances in Intelligent Systems and Computing, vol 255. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1759-6_95
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DOI: https://doi.org/10.1007/978-81-322-1759-6_95
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1758-9
Online ISBN: 978-81-322-1759-6
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