Distance between Two Classes : A Novel Kernel Class Separability Criterion

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概要

• 論文の詳細を見る

• With a Gaussian kernel function, we find that the distance between two classes (DBTC) can be used as a class separability criterion in feature space since the between-class separation and the within-class data distribution are taken into account impliedly. To test the validity of DBTC, we develop a method of tuning the kernel parameters in support vector machine (SVM) algorithm by maximizing the DBTC in feature space. Experimental results on the real-world data show that the proposed method consistently outperforms corresponding hyperparameters tuning methods.

• （社）電子情報通信学会の論文
• 2009-07-01

著者

• Sun Jiancheng

  Key Laboratory Of Biomedical Information Engineering Of Ministry Of Education Xi'an Jiaotong Un

• ZHENG Chongxun

  Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an Jiaotong Univer

• LI Xiaohe

  School of Electronics and Information Engineering, Xi'an Jiaotong University

• Li Xiaohe

  School Of Electronics And Information Engineering Xi'an Jiaotong University

• Zheng Chongxun

  Key Laboratory Of Biomedical Information Engineering Of Ministry Of Education Xi'an Jiaotong Un

関連論文

• Distance between Two Classes : A Novel Kernel Class Separability Criterion

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