A Dynamic Early Stopping Criterion for Random Search in SVM Hyperparameter Optimization

Document Type

Conference Proceeding

Department or Administrative Unit

Computer Science

Publication Date



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.


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.


AIAI 2018: Artificial Intelligence Applications and Innovations


Copyright © 2018, IFIP International Federation for Information Processing