N. Abbas, Y. Chibani, and H. Nemmour, Handwritten Digit Recognition Based on a DSmT-SVM Parallel Combination, 2012 International Conference on Frontiers in Handwriting Recognition, p.241246, 2012.
DOI : 10.1109/ICFHR.2012.208

F. Ardjani and K. Sadouni, Optimization of SVM Multiclass by Particle Swarm (PSO- SVM). I, J.Modern Education and Computer Science, vol.2, p.3238, 2010.

M. Bernard, E. Fromont, A. Habbard, and M. Sebban, Handwritten Digit Recognition using Edit Distance-Based KNN, Teaching Machine Learning Workshop, p.United Kingdom, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00714509

S. Bernard, L. Heutte, and S. Adam, Using Random Forests for Handwritten Digit Recognition, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2, p.10431047, 2007.
DOI : 10.1109/ICDAR.2007.4377074
URL : https://hal.archives-ouvertes.fr/hal-00436372

P. Bilski, Automated Selection of Kernel Parameters in Diagnostics of Analog Systems, Przegl¡d Elektrotechniczny, vol.87, issue.5, p.913, 2011.

C. L. Liu, K. Nakashima, H. Sako, and H. Fujisawa, Handwritten digit recognition: benchmarking of state-of-the-art techniques, Pattern Recognition, vol.36, issue.10, p.22712285, 2003.
DOI : 10.1016/S0031-3203(03)00085-2

P. Chudzian, Optymalizacja Parametrów Przeksztaªcenia J¡drowego w Zadaniach Klasykacji, 2012.

R. Ebrahimzadeh and M. Jampour, Ecient Handwritten Digit Recognition Based on Histogram of Oriented Gradients and SVM, International Journal of Computer Applications, vol.104, issue.9, pp.975-8887, 2014.

M. Hanmandlu and S. Chakraborty, Fuzzy Logic Based Handwritten Character Recognition, International Conference on Image Processing, p.4245, 2001.

W. Homenda, A. Jastrz¦bska, and W. Pedrycz, Rejecting Foreign Elements in Pattern Recognition Problem - Reinforced Training of Rejection Level, Proceedings of the International Conference on Agents and Artificial Intelligence, pp.909910-909922, 2015.
DOI : 10.5220/0005207900900099

C. L. Huang and C. J. Wang, A GA-Based Feature Selection and Parameters Optimization for Support Vector Machines, Expert Systems with Applications 31, p.231240, 2006.

Y. Lecun, L. Jackel, L. Bottou, A. Brunot, C. Cortes et al., A Comparison of Learning Algorithms for Handwritten Digit Recognition, Proceedings of the 1995 International Conference on Articial Neural Networks (ICANN-95), p.5360, 1995.

Y. Lecun, L. Jackel, L. Bottou, A. Brunot, C. Cortes et al., Comparison of Classier Methods: a Case Study in Handwritten Digit Recognition, Proceedings of the 12th International Conference on Pattern Recognition and Neural Networks, 1994.

S. Maji, A. C. Berg, and J. Malik, Classication Using Intersection Kernel Support Vector Machines is Ecient. Computer Vision and Pattern Recognition, CVPR 2008. IEEE Conference, p.18, 2008.

S. Maji and A. C. Berg, Max Margin Additive Classiers for Detection, Proc. International Conference on Computer Vision, 2009.

S. Maji and J. Malik, Fast and Accurate Digit Classication, 2009.

F. T. Shah and K. Yousaf, Handwritten Digit Recognition Using Image Processing and Neural Networks, Proceedings of the World Congress on Engineering, 2007.

P. Shah, S. Karamchandani, T. Nadkar, and N. Gulechha, OCR-Based Chassis- Number Recognition Using Articial Neural Networks, IEEE International Conference on Vehicular Electronics and Safety (ICVES), p.3134, 2009.
DOI : 10.1109/icves.2009.5400240