Abstract: Kohonen self-organization algorithm, known as “topologic maps algorithm”, has been largely used in many applications for classification. However, few theoretical studies have been proposed to improve and optimize the learning process of classification and clustering for dynamic and scalable systems taking into account the evolution of multi-parameter objects. Our objective in this paper is to provide a new approach to improve the accuracy and quality of the classification method based on the basic advantages of the Kohonen self-organization algorithm and on new network functions to pre-eliminate the auto-detected of drawbacks and redundancy.

El Khatir HAIMOUDI, Hanane FAKHOURI, Loubna CHERRAT and Mostafa Ezziyyani, “Towards a New Approach to Improve the Classification Accuracy of the Kohonen’s Self-Organizing Map During Learning Process” International Journal of Advanced Computer Science and Applications(IJACSA), 7(3), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070333

@article{HAIMOUDI2016,
title = {Towards a New Approach to Improve the Classification Accuracy of the Kohonen’s Self-Organizing Map During Learning Process},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070333},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070333},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {3},
author = {El Khatir HAIMOUDI and Hanane FAKHOURI and Loubna CHERRAT and Mostafa Ezziyyani}
}