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Articles by H. Akdag
Total Records ( 3 ) for H. Akdag
  L. Zouaoui , H. Akdag , M. Bedda and M. Boughazi
  This paper deals with the use of the classification rule by an alternative of the nearest neighbors method. We describe first an algorithm based on the partition of the training space in adjacent hyper-cubic cells, then a classification algorithm with two options: one gives an approximate nearest neighbor, the other an exact nearest neighbor. Finally, in a third part, we give results which were obtained by the application on real data. to illustrate the advantage of this method.
  N. Kaddeche , N. Doghmane , M. Kaddeche , H. Akdag and A. Borgi
  We present a method of supervised learning by automatic generation of production rules. This learning method through examples is at the junction of the statistical methods and those based on techniques of Artificial Intelligence. Classification rules are automatically generating rules and show the membership of an object to a class. However, this membership is attached with uncertainty; each conclusion is accompanied by a belief degree. Our approach is polythetic, or multi-feature, insofar as the features which appear in the premises of the rules are selected in block, and not in a successive way. The idea is to locate privileged correlations between the features describing the examples and to gather them by taking account of these correlations. In this article, we propose a new form of correlation search "the Method of Mixed Search "; the originality of this method is to integrate the discriminating capability of classes membership. the search for correlations is based on the union of the classes. Experimental tests were carried out. The principal results obtained as well as their comparison with two other methods of correlations search are presented. These results are satisfactory and allow us to extend the validation of our approach to other data.
  M. Nemissi , H. Seridi and H. Akdag
  This study presents a model of Neuro-Fuzzy classification, which its conception is inspired from the labeled classification using Neural Networks. This last aims to improve the classification performances and to accelerate the training of the used classifier. It is based on the addition of a set of labels to all training examples. Tests will be then carried out with each of these labels to classify a new example. The advantage of this approach is the simplicity of its implementation, which does not require modification of the training algorithm. The proposed model is based on the use of this method with the NFC (Neuro Fuzzy Classifier). To appreciate its performances, tests are carried out on the Iris and human tight data basis by the NFC with and withwout labels.
 
 
 
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