Mamouni El Mamoun1,*, Zennaki Mahmoud1, Sadouni Kaddour1
CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 995-1008, 2019, DOI:10.32604/cmc.2019.08081
Abstract Using Support Vector Machine (SVM) requires the selection of several parameters such as multi-class strategy type (one-against-all or one-against-one), the regularization parameter C, kernel function and their parameters. The choice of these parameters has a great influence on the performance of the final classifier. This paper considers the grid search method and the particle swarm optimization (PSO) technique that have allowed to quickly select and scan a large space of SVM parameters. A comparative study of the SVM models is also presented to examine the convergence speed and the results of each model. SVM is More >
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