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Support Vector Machine (SVM) is the focus of failure diagnose field. There is not a definite theory to guide the choice of its parameters. In this paper, the analysis and research is done to parameter optimization of SVM. The combined algorithm based on Quantum-behavior Particle Swarm Optimization (QPSO) and Simulated Annealing (SA) is present to optimize the parameters of SVM in order to improve...
In this paper a hybrid ensemble particle swarm optimization (HEPSO) algorithm is presented. It combines ensemble learning, subpopulation, part dimensions and random order strategies together. Ensemble learning can help providing a more accurate global guider through combining some previous best positions (pbest) of the particles. The other three strategies increase the diversity. And this algorithm...
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