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Distance between Two Classes: A Novel Kernel Class Separability Criterion

Jiancheng SUN, Chongxun ZHENG, Xiaohe LI

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Summary :

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.

Publication
IEICE TRANSACTIONS on Information Vol.E92-D No.7 pp.1397-1400
Publication Date
2009/07/01
Publicized
Online ISSN
1745-1361
DOI
10.1587/transinf.E92.D.1397
Type of Manuscript
Special Section LETTER (Special Section on Large Scale Algorithms for Learning and Optimization)
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