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Articles by H. Seridi
Total Records ( 2 ) for H. Seridi
  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.
  H. Boudouda , H. Seridi and H.Akdag
  In front of the mass of information which does not cease growing in an exponential way, the human expert is often confronted to data classification problems in the pattern recognition domain. The methods of classification are generally the result of a formalism based on an artificial reasoning, which is at least close to that of a human reasoning. The various approaches suggested in literature, differ the ones from the others by the membership concept of an object to a class; however the initialization method remains ambiguous. In this same study present a new approach of unsupervised automatic classification under the C-Means family. This new approach based on the fusion of fuzzy and the possibility theory, allows on the one hand to solve, simultaneously the problem of coincidence and the noise and on the other hand to accelerate classification. The initialization methodology used in this study is based on probabilistic membership matrix. To show the performances of this new approach, tests were carried out on the Iris data basis.
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