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## The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines

#### Aslı YAMAN [1] , Mehmet Ali CENGİZ [2]

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.
Support vector machines, kernel function, tune parameter
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Primary Language en Mathematics, Interdisciplinary Applications, Mathematics, Applied Research Article Orcid: 0000-0003-2886-6765Author: Aslı YAMAN (Primary Author)Institution: ONDOKUZ MAYIS UNIVERSITYCountry: Turkey Orcid: 0000-0002-1271-2588Author: Mehmet Ali CENGİZInstitution: ONDOKUZ MAYIS UNIVERSITYCountry: Turkey Publication Date : March 30, 2021
 Bibtex @research article { jnt876920, journal = {Journal of New Theory}, issn = {}, eissn = {2149-1402}, address = {Mathematics Department, Gaziosmanpasa University 60250 Tokat-TURKEY.}, publisher = {Gaziosmanpasa University}, year = {2021}, volume = {}, pages = {64 - 71}, doi = {}, title = {The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines}, key = {cite}, author = {Yaman, Aslı and Cengiz, Mehmet Ali} } APA Yaman, A , Cengiz, M . (2021). The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines . Journal of New Theory , (34) , 64-71 . Retrieved from https://dergipark.org.tr/en/pub/jnt/issue/61070/876920 MLA Yaman, A , Cengiz, M . "The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines" . Journal of New Theory (2021 ): 64-71 Chicago Yaman, A , Cengiz, M . "The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines". Journal of New Theory (2021 ): 64-71 RIS TY - JOUR T1 - The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines AU - Aslı Yaman , Mehmet Ali Cengiz Y1 - 2021 PY - 2021 N1 - DO - T2 - Journal of New Theory JF - Journal JO - JOR SP - 64 EP - 71 VL - IS - 34 SN - -2149-1402 M3 - UR - Y2 - 2021 ER - EndNote %0 Journal of New Theory The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines %A Aslı Yaman , Mehmet Ali Cengiz %T The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines %D 2021 %J Journal of New Theory %P -2149-1402 %V %N 34 %R %U ISNAD Yaman, Aslı , Cengiz, Mehmet Ali . "The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines". Journal of New Theory / 34 (March 2021): 64-71 . AMA Yaman A , Cengiz M . The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines. JNT. 2021; (34): 64-71. Vancouver Yaman A , Cengiz M . The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines. Journal of New Theory. 2021; (34): 64-71. IEEE A. Yaman and M. Cengiz , "The Effects of Kernel Functions and Optimal Hyperparameter Selection on Support Vector Machines", Journal of New Theory, no. 34, pp. 64-71, Mar. 2021

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