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The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines
ISSN: 2149 - 1402
Publisher: author
The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines
Indexed in
Science General
ARTICLE-FACTOR
1.3
Article Basics Score: 2
Article Transparency Score: 2
Article Operation Score: 3
Article Articles Score: 2
Article Accessibility Score: 3
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International Category Code (ICC):
ICC-1402
Publisher: Journal Of New Theory Naim Çağman
International Journal Address (IAA):
IAA.ZONE/214985411402
eISSN
:
2149 - 1402
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Abstract
Support Vector Machine (SVM) is a supervised machine learning method used for classification and regression. It is based on the Vapnik-Chervonenkis (VC) theory and Structural Risk Minimization (SRM) principle. Thanks to its strong theoretical background, SVM exhibits a high performance compared to many other machine learning methods. The selection of hyperparameters and the kernel functions is an important task in the presence of SVM problems. In this study, the effect of tuning hyperparameters and sample size for the kernel functions on SVM classification accuracy was investigated. For this, UCI datasets of different sizes and with different correlations were simulated. Grid search and 10-fold Cross-Validation methods were used to tune the hyperparameters. Then, SVM classification process was performed using three kernel functions, and classification accuracy values were examined.
