Parameter selection of support vector machine based on chaotic particle swarm optimization algorithm

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 pa...

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Hauptverfasser:	Jingming Peng, Shuzhou Wang
Format:	Tagungsbericht
Sprache:	chi ; eng
Schlagworte:	Automation chaotic optimization Convergence Cybernetics parameter selection Particle swarm optimization Statistical learning support vector machine Support vector machine classification
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