Deep Support Vector Classification and Regression
Entidad
UAM. Departamento de Ingeniería InformáticaFecha de edición
2019-05-01Cita
10.1007/978-3-030-19651-6_4
From Bioinspired Systems and Biomedical Applications to Machine Learning, IWINAC, Lecture Notes in Computer Science, Volume 11487. Springer 2019. 33-43
ISBN
978-3-030-19651-6 (online)DOI
10.1007/978-3-030-19651-6_4Financiado por
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 UAMProyecto
Gobierno de España. TIN2016-76406-P; Comunidad de Madrid. 82013/ICE-2845/CASI-CAM-CMVersión del editor
https://doi.org/10.1007/978-3-030-19651-6_4Materias
InformáticaNota
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_4Derechos
© Springer NatureResumen
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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Google Scholar:Díaz-Vico, David
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Prada, Jesús
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Omari, Adil
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Dorronsoro Ibero, José Ramón
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