Skip to Main content Skip to Navigation
Journal articles

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.
Document type :
Journal articles
Complete list of metadata

https://hal-amu.archives-ouvertes.fr/hal-03617299
Contributor : Mohamed Quafafou Connect in order to contact the contributor
Submitted on : Wednesday, March 23, 2022 - 1:44:33 PM
Last modification on : Friday, April 1, 2022 - 3:58:07 AM

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Links full text

Identifiers

Collections

Citation

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⟩

Share

Metrics

Record views

12