Evaluating hyper-parameter tuning using random search in support vector machines for software effort estimation-Reference-Cited by-同舟云学术

Evaluating hyper-parameter tuning using random search in support vector machines for software effort estimation

Published:2020-11-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 16th ACM International Conference on Predictive Models and Data Analytics in Software Engineering
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Author:

Villalobos-Arias Leonardo¹,Quesada-López Christian¹,Guevara-Coto Jose¹,Martínez Alexandra¹,Jenkins Marcelo¹

Affiliation:

1. University of Costa Rica, Costa Rica

Publisher

ACM

Reference38 articles.

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2. Chris Albon. 2018. Machine learning with python cookbook: Practical solutions from preprocessing to deep learning. " O'Reilly Media Inc.". Chris Albon. 2018. Machine learning with python cookbook: Practical solutions from preprocessing to deep learning. " O'Reilly Media Inc.".

3. Random search for hyper-parameter optimization;Bergstra James;Journal of machine learning research 13,2012

4. James S Bergstra Rémi Bardenet Yoshua Bengio and Balázs Kégl. 2011. Algorithms for hyper-parameter optimization. In Advances in neural information processing systems. 2546-2554. James S Bergstra Rémi Bardenet Yoshua Bengio and Balázs Kégl. 2011. Algorithms for hyper-parameter optimization. In Advances in neural information processing systems. 2546-2554.

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