Title :
Effectiveness of Random Search in SVM hyper-parameter tuning
Author :
Rafael G. Mantovani;André L. D. Rossi;Joaquin Vanschoren;Bernd Bischl;André C. P. L. F. de Carvalho
Author_Institution :
Universidade de Sã
fDate :
7/1/2015 12:00:00 AM
Abstract :
Classification is one of the most common machine learning tasks. SVMs have been frequently applied to this task. In general, the values chosen for the hyper-parameters of SVMs affect the performance of their induced predictive models. Several studies use optimization techniques to find a set of hyper-parameter values that induces classifiers with good predictive performance. This paper investigates the hypothesis that a simple Random Search method is sufficient to adjust the hyper-parameters of SVMs. A set of experiments compared the performance of five tuning techniques: three meta-heuristics commonly used, Random Search and Grid Search. The experimental results show that the predictive performance of models using Random Search is equivalent to those obtained using meta-heuristics and Grid Search, but with a lower computational cost.
Keywords :
"Accuracy","Heating","Support vector machines","Computational modeling","Lead","Training","Blogs"
Conference_Titel :
Neural Networks (IJCNN), 2015 International Joint Conference on
Electronic_ISBN :
2161-4407
DOI :
10.1109/IJCNN.2015.7280664