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: W.e.i. Jiang, Sauleh Siddiqui
Formato: article
Lenguaje:EN
Publicado: Elsevier 2020
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Acceso en línea:https://doaj.org/article/f82d7c90108a43cf8c8fd9386871b915
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