Deep support vector classification and regression

  1. David Díaz-Vico
  2. Jesús Prada
  3. Adil Omari
  4. José R. Dorronsoro
Liburua:
From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II
  1. José Manuel Ferrández Vicente (dir. congr.)
  2. José Ramón Álvarez-Sánchez (dir. congr.)
  3. Félix de la Paz López (dir. congr.)
  4. Javier Toledo Moreo (dir. congr.)
  5. Hojjat Adeli (coord.)

Argitaletxea: Springer Suiza

ISBN: 978-3-030-19651-6

Argitalpen urtea: 2019

Orrialdeak: 33-43

Mota: Liburuko kapitulua

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Laburpena

Support Vector Machines, SVM, are one of the most popularmachine 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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