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Journal Articles Journal of Physics: Conference Series Year : 2021

Non-smooth classification model based on new smoothing technique

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.

Dates and versions

hal-03617299 , version 1 (23-03-2022)

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S. Lyaqini, M. Nachaoui, M. Quafafou. Non-smooth classification model based on new smoothing technique. Journal of Physics: Conference Series, 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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