Minimum distance classifier based on between-class and within-class scatter is proposed.

提出了一种基于类内类间离散度的最小距离分类器设计方法。

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The experimental results show that the KDDA features achieve the best recognition performance, and the SVM classifier outperforms the minimum Euclidean distance classifier.

结果表明，SVM分类器的识别性能优于最小欧氏距离分类器，且KDDA特征的识别性能最优。

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