Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

book chapter · IGI Global eBooks · 2011

Kernel Width Selection for SVM Classification

A.J. Smith, Kate Smith‐Miles

Cited by 0Abstract and details only

Abstract

The most critical component of kernel based learning algorithms is the choice of an appropriate kernel and its optimal parameters. In this paper we propose a rule based meta-learning approach for automatic radial basis function (rbf) kernel and its parameter selection for Support Vector Machine (SVM) classification. First, the best parameter selection is considered on the basis of prior information of the data with the help of Maximum Likelihood (ML) method and Nelder-Mead (N-M) simplex method. Then the new rule based meta-learning approach is constructed and tested on different sizes of 112 datasets with binary class as well as multi class classification problems. We observe that our rule based methodology provides significant improvement of computational time as well as accuracy in some specific cases.

Cite this paper

APA 7

Smith, A., & Smith‐Miles, K. (2011). Kernel Width Selection for SVM Classification. IGI Global eBooks, 101–115. https://doi.org/10.4018/978-1-59904-528-3.ch006

MLA 9

Smith, A.J., and Kate Smith‐Miles. “Kernel Width Selection for SVM Classification.” IGI Global eBooks, 2011, pp. 101–15, https://doi.org/10.4018/978-1-59904-528-3.ch006.

Chicago

Smith, A.J., and Kate Smith‐Miles. 2011. “Kernel Width Selection for SVM Classification.” IGI Global eBooks: 101–15. https://doi.org/10.4018/978-1-59904-528-3.ch006.

Harvard

Smith, A. and Smith‐Miles, K. (2011) ‘Kernel Width Selection for SVM Classification’, IGI Global eBooks, pp. 101–115. Available at: https://doi.org/10.4018/978-1-59904-528-3.ch006.

BibTeX
@incollection{smith2011kernel,
  title = {{Kernel Width Selection for SVM Classification}},
  author = {Smith, A.J. and Smith‐Miles, Kate},
  booktitle = {IGI Global eBooks},
  pages = {101--115},
  year = {2011},
  doi = {10.4018/978-1-59904-528-3.ch006}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

Related papers