Proceedings Article10.1109/ICCTA.2012.6523566
Parameter determination of support vector machine using scatter search approach
Mohammed H. Afif,A-R Hedar,Taysir H. Abdel Hamid,Yousef B. Mahdy +3 more
- 01 Oct 2012
- pp 181-186
6
TL;DR: A scatter search approach with support vector machine using three different kernel functions, shortly (3SVM) proves that the 3SVM is a promising approach and has a competitive performance relative to some other published methods.
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Abstract: Support Vector Machine (SVM) is a popular data classification method with many diverse applications SVM has many parameters, which have significant influences on the performance of SVM classifier In this paper, a Scatter Search approach is used 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 using three different kernel functions, shortly (3SVM) To evaluate the performance of the proposed method, 4 benchmark datasets are used Experiments and comparisons prove that the 3SVM is a promising approach and has a competitive performance relative to some other published methods
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A Brief Overview on Parameter Optimization of Support Vector Machine
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TL;DR: The objective of this paper is to provide readers a brief overview of the recent advances for parameter optimization of SVM and enable them to develop and implement new optimization strategies for SVM-related research at their disposal.
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A brief overview on parameter optimization of support vector machine
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- 01 Jan 2014
TL;DR: The objective of this paper is to provide readers a brief overview of the recent advances for parameter optimization of SVM and enable them to develop and implement new optimization strategies for SVM-related research at their disposal.
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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.
Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks
Javed Khan,Jun S. Wei,Markus Ringnér,Markus Ringnér,Lao H. Saal,Marc Ladanyi,Frank Westermann,Frank Berthold,Manfred Schwab,Cristina R. Antonescu,Carsten Peterson,Paul S. Meltzer +11 more
TL;DR: The ability of the trained ANN models to recognize SRBCTs is demonstrated, and the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy are demonstrated.
2.9K
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.
2.1K