Hyper-parameter optimization for support vector machines using stochastic gradient descent and dual coordinate descent
We developed a gradient-based method to optimize the regularization hyper-parameter, C, for support vector machines in a bilevel optimization framework. On the upper level, we optimized the hyper-parameter C to minimize the prediction loss on validation data using stochastic gradient descent. On the...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2020
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/f82d7c90108a43cf8c8fd9386871b915 |
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