]>Combining Dissimilarities in a Hyper Reproducing Kernel Hilbert Space for Complex Human Cancer Prediction : Table 3
Table 3: Accuracy for the 𝜈 -SVM using a linear combination of non-Euclidean dissimilarities in an HRKHS. The 𝜈 -SVM based on the best distance, the classical 𝜈 -SVM, and the Lanckriet formalism have been taken as a reference.

Technique Breast B DLBCL C DLBCL D

𝜈 -SVM (Coordinates) 1 0 . 2 0 % 6 . 8 9 % 1 2 . 9 6 %
𝜈 -SVM (Best Distance) 8 . 6 % 6 . 8 9 % 1 4 . 8 1 %
𝜈 -SVM (Nonlinear kernel) 8 . 1 6 % 6 . 8 9 % 1 2 . 9 6 %
Lanckriet (finite family) 8 % 1 0 . 3 % 2 5 . 2 %

Infinite family of distances 6 % 5 . 3 3 % 1 6 %