A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Geometry and invariance in kernel based methods
Advances in kernel methods
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Generalisation Error Bounds for Sparse Linear Classifiers
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Exact simplification of support vector solutions
The Journal of Machine Learning Research
Adaptive simplification of solution for support vector machine
Pattern Recognition
Particle swarm optimisation with spatial particle extension
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Extending particle swarm optimisers with self-organized criticality
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Optimization using particle swarms with near neighbor interactions
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A sequential algorithm for sparse support vector classifiers
Pattern Recognition
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The main issue is to search for a subset of the support vector solutions produced by an SVM that forms a discriminant function best approximating the original one. The work is accomplished by giving a fitness (objective function) that fairly indicates how well the discriminant function formed by a set of selected vectors approximates the original one, and searching for the set of vectors having the best fitness using PSO, EGA, or a hybrid approach combining PSO and EGA. Both the defined fitness function and the adopted search technique affect the performance. Our method can be applied to SVMs associated with any general kernel. The reduction rate can be adaptively adjusted based on the requirement of the task. The proposed approach is tested on some benchmark datasets. The experimental results show that the proposed method using PSO, EGA, or a hybrid strategy combining PSO and EGA associated with the objective function defined in the paper outperforms both the method proposed by Li et al. (2007) and our previously proposed method (Lin and Yeh, 2009), and that a hybrid strategy of PSO and EGA provides better results than a single strategy of PSO or EGA.