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Non-smooth classification model based on new smoothing technique

S. Lyaqini 1, * M. Nachaoui 1 M. Quafafou 2 
* Corresponding author
2 DANA - Data Mining at scale
LIS - Laboratoire d'Informatique et Systèmes
Abstract : This work describes a framework for solving support vector machine with kernel (SVMK). Recently, it has been proved that the use of non-smooth loss function for supervised learning problem gives more efficient results [1]. This gives the idea of solving the SVMK problem based on hinge loss function. However, the hinge loss function is non-differentiable (we can’t use the standard optimization methods to minimize the empirical risk). To overcome this difficulty, a special smoothing technique for the hinge loss is proposed. Thus, the obtained smooth problem combined with Tikhonov regularization is solved using a stochastic gradient descent method. Finally, some numerical experiments on academic and real-life datasets are presented to show the efficiency of the proposed approach.
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Submitted on : Wednesday, March 23, 2022 - 1:44:33 PM
Last modification on : Friday, April 1, 2022 - 3:58:07 AM


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S. Lyaqini, M. Nachaoui, M. Quafafou. Non-smooth classification model based on new smoothing technique. Journal of Physics: Conference Series, IOP Publishing, 2021, The International Conference on Mathematics & Data Science (ICMDS) 2020, 1743 (1), pp.012025. ⟨10.1088/1742-6596/1743/1/012025⟩. ⟨hal-03617299⟩



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