Deep Support Vector Classification and Regression

Díaz-Vico, David; Prada, Jesús; Omari, Adil; Dorronsoro Ibero, José Ramón

UAM_Biblioteca

Author

Díaz-Vico, David; Prada, Jesús; Omari, Adil; Dorronsoro Ibero, José Ramón

Entity

UAM. Departamento de Ingeniería Informática

Date

2019-05-01

Citation

Deep Support Vector Classification and Regression. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning, IWINAC, Lecture Notes in Computer Science 11487 (2019): 33-43

ISBN

978-3-030-19651-6 (online)

DOI

10.1007/978-3-030-19651-6_4

Funded by

With partial support from Spain's grants TIN2016-76406-P and 82013/ICE-2845 CASI-CAM-CM. Work partially supported also by project FACILAyudas Fundación BBVA a Equipos de Investigación Científica 2016, and the UAMADIC Chair for Data Science and Machine Learning. We also gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM

Project

Gobierno de España. TIN2016-76406-P; Comunidad de Madrid. 82013/ICE-2845 CASI-CAM-CM

Editor's Version

https://doi.org/10.1007/978-3-030-19651-6_4

Subjects

Informática

URI

http://hdl.handle.net/10486/702829

Note

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-19651-6_4

Rights

Abstract

Support Vector Machines, SVM, are one of the most popular machine learning models for supervised problems and have proved to achieve great performance in a wide broad of predicting tasks. However, they can suffer from scalability issues when working with large sample sizes, a common situation in the big data era. On the other hand, Deep Neural Networks (DNNs) can handle large datasets with greater ease and in this paper we propose Deep SVM models that combine the highly non-linear feature processing of DNNs with SVM loss functions. As we will show, these models can achieve performances similar to those of standard SVM while having a greater sample scalability

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