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Kernel Parameter Selection for SVM Classification: Adaboost Approach

Kernel Parameter Selection for SVM Classification: Adaboost Approach

Manju Bala, R. K. Agrawal
Copyright: © 2010 |Pages: 12
ISBN13: 9781615207534|ISBN10: 1615207538|ISBN13 Softcover: 9781616922672|EISBN13: 9781615207541
DOI: 10.4018/978-1-61520-753-4.ch002
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MLA

Bala, Manju, and R. K. Agrawal. "Kernel Parameter Selection for SVM Classification: Adaboost Approach." Strategic Pervasive Computing Applications: Emerging Trends, edited by Varuna Godara, IGI Global, 2010, pp. 44-55. https://doi.org/10.4018/978-1-61520-753-4.ch002

APA

Bala, M. & Agrawal, R. (2010). Kernel Parameter Selection for SVM Classification: Adaboost Approach. In V. Godara (Ed.), Strategic Pervasive Computing Applications: Emerging Trends (pp. 44-55). IGI Global. https://doi.org/10.4018/978-1-61520-753-4.ch002

Chicago

Bala, Manju, and R. K. Agrawal. "Kernel Parameter Selection for SVM Classification: Adaboost Approach." In Strategic Pervasive Computing Applications: Emerging Trends, edited by Varuna Godara, 44-55. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-753-4.ch002

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Abstract

The choice of kernel function and its parameter is very important for better performance of support vector machine. In this chapter, the authors proposed few new kernel functions which satisfy the Mercer’s conditions and a robust algorithm to automatically determine the suitable kernel function and its parameters based on AdaBoost to improve the performance of support vector machine. The performance of proposed algorithm is evaluated on several benchmark datasets from UCI repository. The experimental results for different datasets show that the Gaussian kernel is not always the best choice to achieve high generalization of support vector machine classifier. However, with the proper choice of kernel function and its parameters using proposed algorithm, it is possible to achieve maximum classification accuracy for all datasets.

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