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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.1
- 수록면
- 333 - 354 (22page)
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초록· 키워드
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A support vector machine (SVM) is a state-of-the-art machine learning model rooted in structural risk minimization. SVM is underestimated with regards to its application to real world problems because of the difficulties associated with its use. We aim at showing that the performance of SVM highly depends on which kernel function to use. To achieve these, after providing a summary of support vector machines and kernel function, we constructed experiments with various benchmark datasets to compare the performance of various kernel functions. For evaluating the performance of SVM, the F1-score and its Standard Deviation with 10-cross validation was used. Furthermore, we used taylor diagrams to reveal the difference between kernels. Finally, we provided Python codes for all our experiments to enable re-implementation of the experiments.
목차
등록된 정보가 없습니다.
참고문헌 (95)
참고문헌 신청
Boser, Bernhard E / 1992 / A training algorithm for optimal margin classifiers / Proceedings of the fifth annual workshop on Computational learning theory :144~152
Smola / 2004 / A tutorial on support vector regression / Statistics and computing 14(3):199~222
Burges, Christopher JC / 1998 / A tutorial on support vector machines for pattern recognition / Data mining and knowledge discovery 2(2):121~167
Guyon, I / Svm application list
Wang, Guosheng / 2008 / A survey on training algorithms for support vector machine classifiers / Fourth International Conference on Networked Computing and Advanced Information Management 1:123~128
Smola / 2004 / A tutorial on support vector regression / Statistics and computing 14(3):199~222
Burges, Christopher JC / 1998 / A tutorial on support vector machines for pattern recognition / Data mining and knowledge discovery 2(2):121~167
Guyon, I / Svm application list
Wang, Guosheng / 2008 / A survey on training algorithms for support vector machine classifiers / Fourth International Conference on Networked Computing and Advanced Information Management 1:123~128
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