Ilyes Rebai

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Multiple kernel learning (MKL) approach has been proposed for kernel methods and has shown high performance for solving some real-world applications. It consists on learning the optimal kernel from one layer of multiple predefined kernels. Unfortunately, this approach is not rich enough to solve relatively complex problems. With the emergence and the(More)
We investigate how the three technologies, social media, mobile / pervasive learning and semantic web, may enhance Inquiry-Based Science Teaching (IBST) approaches and digital literacy. IBST may be defined by engaging students in: i) authentic and problembased activities, ii) experimental procedures, iii) self regulated learning sequences, iv) discursive(More)
With the increasing number of users of text to speech applications, high quality speech synthesis is required. However, only few researches concern Arabic text to speech applications. Compared with other languages such as English and French the quality of Arabic synthesis speech is still poor. For these reasons, we propose in this paper an Arabic text to(More)
Deep learning techniques have claimed state-of-the-art results in a wide range of tasks, including classification. Despite the promising results, there are limitations for these large networks. In fact, deep neural networks have a poor generalisation performance on small data sets, such as biologic data. This paper describes a new machine learning algorithm(More)
Kernel Methods have been successfully applied in different tasks and used on a variety of data sample sizes. Multiple Kernel Learning (MKL) and Multilayer Multiple Kernel Learning (MLMKL), as new families of kernel methods, consist of learning the optimal kernel from a set of predefined kernels by using an optimization algorithm. However, learning this(More)
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