TY  - EJOU
AU  - Mamoun, i El 
AU  - Mahmoud, Zennaki 
AU  - Kaddour, Sadouni 

TI  - SVM Model Selection Using PSO for Learning Handwritten Arabic Characters
T2  - Computers, Materials \& Continua

PY  - 2019
VL  - 61
IS  - 3
SN  - 1546-2226

AB  - 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 applied to handwritten Arabic characters learning, with a database containing 4840 Arabic characters in their different positions (isolated, beginning, middle and end). Some very promising results have been achieved.
KW  - SVM
KW  -  PSO
KW  -  handwritten Arabic
KW  -  grid search
KW  -  character recognition

DO  - 10.32604/cmc.2019.08081