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- W1981799260 abstract "Support vector machine (SVM) are new methods based on statistical learning theory. Training SVM can be formulated as a quadratic programming problem. The parameter selection of SVM should to be done before resolving the QP problem. Particle swarm optimization (POS) algorithm was adpoted to select parameters of SVM. To improve its global search ability, POS algorithm was modified by virtue of chaotic motion with sensitive dependence on initial conditions and ergodicity. It is shown by simulation that the chaotic POS algorithm can derive a set of optimal parameters." @default.
- W1981799260 created "2016-06-24" @default.
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- W1981799260 date "2010-07-01" @default.
- W1981799260 modified "2023-09-25" @default.
- W1981799260 title "Parameter selection of support vector machine based on chaotic particle swarm optimization algorithm" @default.
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- W1981799260 doi "https://doi.org/10.1109/wcica.2010.5555055" @default.
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