Distance between Two Classes: A Novel Kernel Class Separability Criterion
Jiancheng Sun, Chongxun Zheng, Xiaohe Li · IEICE Transactions on Information and Systems · 2009
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.