Title
A Dynamic Early Stopping Criterion for Random Search in SVM Hyperparameter Optimization
Document Type
Conference Proceeding
Department or Administrative Unit
Computer Science
Publication Date
2018
Abstract
We introduce a dynamic early stopping condition for Random Search optimization algorithms. We test our algorithm for SVM hyperparameter optimization for classification tasks, on six commonly used datasets. According to the experimental results, we reduce significantly the number of trials used. Since each trial requires a re-training of the SVM model, our method accelerates the RS optimization. The code runs on a multi-core system and we analyze the achieved scalability for an increasing number of cores.
Recommended Citation
Florea A.C., Andonie R. (2018) A Dynamic Early Stopping Criterion for Random Search in SVM Hyperparameter Optimization. In: Iliadis L., Maglogiannis I., Plagianakos V. (eds) Artificial Intelligence Applications and Innovations. AIAI 2018. IFIP Advances in Information and Communication Technology, vol 519. Springer, Cham. https://doi.org/10.1007/978-3-319-92007-8_15
Journal
AIAI 2018: Artificial Intelligence Applications and Innovations
Rights
Copyright © 2018, IFIP International Federation for Information Processing
Comments
This article was originally published in AIAI 2018: Artificial Intelligence Applications and Innovations. The full-text article from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.