Proceedings Article10.1109/JEC-ECC.2012.6186977
Data classification using Support Vector Machine integrated with scatter search method
Mohammed H. Afif,Abdel-Rahman Hedar +1 more
- 06 Mar 2012
- pp 168-172
31
TL;DR: A meta-heuristic approach (Scatter Search) is employed to find near optimal values of the SVM parameters, and its kernel parameters and proves that the proposed method is promising and has competitive performance.
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Abstract: Support Vector Machine (SVM) is a popular pattern classification method with many diverse applications. The SVM has many parameters, which have significant influences the performance of SVM classifier. In this paper, we employ a meta-heuristic approach (Scatter Search) to find near optimal values of the SVM parameters, and its kernel parameters. The proposed method integrates a scatter search approach with support vector machine, shortly (3SVM). To evaluate the performance of the proposed method, 9 datasets from LibSVM tool webpage [2] were used. Experiments prove that the proposed method is promising and has competitive performance.
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References
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Practical selection of SVM parameters and noise estimation for SVM regression
Vladimir Cherkassky,Yunqian Ma +1 more
TL;DR: This work describes a new analytical prescription for setting the value of insensitive zone epsilon, as a function of training sample size, and compares generalization performance of SVM regression under sparse sample settings with regression using 'least-modulus' loss (epsilon=0) and standard squared loss.
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Asymptotic behaviors of support vector machines with Gaussian kernel
S. Sathiya Keerthi,Chih-Jen Lin +1 more
TL;DR: The behavior of the SVM classifier when these hyper parameters take very small or very large values is analyzed, which helps in understanding thehyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors.