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Reducing dimension processing is needed in feature samples because the repeated and secondary features would reduce the classification ability and increase computation complexity. In this paper, a feature selection method, named MPSO (Modified Particle Swarm Optimization), is proposed. The original group velocity of a particle swarm was changed into two separate and parallel particle swarm velocity,...
Support vector machine (SVM) has become a popular classification tool, but training SVM consumes large memory and computation time. Traditional methods can not overcome above shortcomings. This paper presents a novel SVM training method based improved chaotic particle swarm optimization (CPSO) algorithm. Firstly, a new chaotic search model using improved circle map is introduced. Then this new model...
For the feature selection and parameter optimization of LS-SVM, propose a At first, a population of particles (feature subsets) was randomly generated, then the features and parameters are optimized by PSO algorithm. The experiments on the UCI database indicate that the proposed method can efficiently find the suitable feature subsets and LS-SVM parameters. Also, comparison are made against GALS-SVM...
Support vector machines (SVM) can overcome the disadvantage of traditional anomaly detection, which need large sample data and have great effect in real-time detection, but has the disadvantage of slow training velocity. Least squares support vector machines (LS-SVM) can overcome the disadvantage of slow training velocity, but makes the solution lose sparsity and robustness. So a weighted LS-SVM (WLS-SVM)...
This paper presents a new SVM algorithm framework optimized by PSO algorithm. The value of parameters in the SVM has great influence on the performance of regression model. In previous works the choice of these parameters mainly depends on the experience. In our work PSO algorithm was used to optimize these parameters to form a new SVM framework - PSVM. The proposed algorithm was used to forecast...
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