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- W2069802948 abstract "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." @default.
- W2069802948 created "2016-06-24" @default.
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- W2069802948 date "2009-01-01" @default.
- W2069802948 modified "2023-10-05" @default.
- W2069802948 title "Distance between Two Classes: A Novel Kernel Class Separability Criterion" @default.
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- W2069802948 doi "https://doi.org/10.1587/transinf.e92.d.1397" @default.
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