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...

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Main Authors: Zhiliang Liu, Hongbing Xu
פורמט: Article
שפה:English
יצא לאור: SAGE Publishing 2014-06-01
סדרה:Journal of Algorithms & Computational Technology
גישה מקוונת:https://doi.org/10.1260/1748-3018.8.2.163