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...
| 主要な著者: | , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
SAGE Publishing
2014-06-01
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| シリーズ: | Journal of Algorithms & Computational Technology |
| オンライン･アクセス: | https://doi.org/10.1260/1748-3018.8.2.163 |