A Dynamic Early Stopping Criterion for Random Search in SVM Hyperparameter Optimization
Department or Administrative Unit
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.
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
AIAI 2018: Artificial Intelligence Applications and Innovations
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