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. 2012 Sep 6;28(21):2824–2833. doi: 10.1093/bioinformatics/bts509

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Fig. 2. — Joint distribution of the true and estimated errors for n = 30, 60, 120, d = 5, two classification rules (LDA and RBF-SVM) and two error estimation methods (5F-CV and LOO). The covariance matrices are equal with features uncorrelated for LDA and unequal with features correlated for RBF-SVM: (a) n = 30, LDA, 5F-CV, (b) n = 60, LDA, 5F-CV, (c) n = 120, LDA, 5F-CV, (d) n = 30, RBF-SVM, LOO, (e) n = 60, RBF-SVM, LOO, (f) n = 120, RBF-SVM, LOO, The white line shows the axis, the dotted line shows the regression line and the circle indicates the sample mean of the joint distribution

Inline graphic — Joint distribution of the true and estimated errors for n = 30, 60, 120, d = 5, two classification rules (LDA and RBF-SVM) and two error estimation methods (5F-CV and LOO). The covariance matrices are equal with features uncorrelated for LDA and unequal with features correlated for RBF-SVM: (a) n = 30, LDA, 5F-CV, (b) n = 60, LDA, 5F-CV, (c) n = 120, LDA, 5F-CV, (d) n = 30, RBF-SVM, LOO, (e) n = 60, RBF-SVM, LOO, (f) n = 120, RBF-SVM, LOO, The white line shows the axis, the dotted line shows the regression line and the circle indicates the sample mean of the joint distribution