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• W1629102637 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." @default.
• W1629102637 created "2016-06-24" @default.
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• W1629102637 date "2015-07-01" @default.
• W1629102637 modified "2023-10-16" @default.
• W1629102637 title "Effectiveness of Random Search in SVM hyper-parameter tuning" @default.
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• W1629102637 doi "https://doi.org/10.1109/ijcnn.2015.7280664" @default.
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