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Klosgen W and Zytkow J M (eds.), Handbook of data mining and knowledge discovery, OUP, Oxford, 2002.

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Comparison of Kernel Selection for Support Vector Machines Using Diabetes Dataset

1Department of Computer Science & Engineering, Orissa Engineering College, Bhubaneswar, Odisha (India) under Biju Patnaik University of Technology, Odisha

2Department of Computer Science & Engineering, SriSri University, Bhubaneswar, Odisha, India


Journal of Computer Sciences and Applications. 2015, Vol. 3 No. 6, 181-184
DOI: 10.12691/jcsa-3-6-14
Copyright © 2016 Science and Education Publishing

Cite this paper:
Tapas Ranjan Baitharu, Subhendu Ku. Pani, Sunil kumar Dhal. Comparison of Kernel Selection for Support Vector Machines Using Diabetes Dataset. Journal of Computer Sciences and Applications. 2015; 3(6):181-184. doi: 10.12691/jcsa-3-6-14.

Correspondence to: Subhendu  Ku. Pani, Department of Computer Science & Engineering, Orissa Engineering College, Bhubaneswar, Odisha (India) under Biju Patnaik University of Technology, Odisha. Email: skpani.india@gmail.com

Abstract

One of the major problems in the study of Support vector machine (SVM) is kernel selection, that’s based necessarily on the problem of deciding a kernel function for a particular task and dataset. By contradiction to other machine learning algorithms, SVM focuses on maximizing the generalisation ability, which depends on the empirical risk and the complexity of the machine. We were focused on SVM trained using linear, polynomial, puk and Radial Basic Function (RBF) kernels. A preliminary study has been made between SVM using the best choice of kernel. Results had revealed that SVM trained using Linear Kernel is the best choice for dealing with Diabetes dataset.

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