Kernel Parameter Selection for Support Vector Machine Classification
Parameter selection for kernel functions is important to the robust classification performance of a support vector machine (SVM). This paper introduces a parameter selection method for kernel functions in SVM. The proposed method tries to estimate the class separability by cosine similarity in the k...
| Huvudupphovsmän: | , |
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| Materialtyp: | Artikel |
| Språk: | English |
| Publicerad: |
SAGE Publishing
2014-06-01
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| Serie: | Journal of Algorithms & Computational Technology |
| Länkar: | https://doi.org/10.1260/1748-3018.8.2.163 |