Salima Hassairi

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This paper gives a review of the deep learning history and proposes a new approach to supervised image classification by the combination of two techniques of learning: the wavelet network and the deep learning. This new approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a(More)
In this paper, a new approach to supervised image classification is suggested. It's conducted by the combination of two techniques of learning: the wavelet network and the deep learning. This new approach consists of performing the classification of one class versus all the other classes of the dataset by the reconstruction of a convolutional deep neural(More)
The major issue in pattern classification is in the extraction of features in the training phase. The focus of this work is on combining the ability of wavelet networks and the deep learning techniques to propose a new supervised feature extraction method to pattern classification. This new approach allows the classification of all classes of the dataset by(More)
The goal of the Deep learning methods is learning feature hierarchies with features from higher levels to lower level features of the hierarchy. The major contribution of this paper is to show how to extract features and train an image classification system on large-scale datasets. This method is an improvement of our recent work. The training is carried(More)
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