Table 3.
Effects of sequentially adding data sources (continued)
| Data sources | MKLdiv-dc | MKLdiv-conv | SimpleMKL | MKL-RKDA |
|---|---|---|---|---|
| SW1 | 62.92 | 62.92 | 62.40 | 61.87 |
| SW1S | 65.27 (24.72 s) |
66.31 (10.49 s) |
64.22 (40.60 s) |
64.75 (0.12 s) |
| SW1SW2S | 67.10 (48.79 s) |
66.05 (4.65 s) |
64.75 (61.71 s) |
64.49 (0.15 s) |
| SW1SW2CS | 73.36 (40.65 s) |
72.32 (23.43 s) |
65.01 (62.81 s) |
67.62 (0.17 s) |
| SW1SW2CSH | 74.67 (72.19 s) |
72.32 (8.69 s) |
66.31 (75.11 s) |
67.88 (0.15 s) |
| SW1SW2CSHP | 74.93 (123.98 s) |
74.41 (11.63 s) |
66.31 (74.85 s) |
69.71 (0.18 s) |
| SW1SW2CSHPZ |
75.19 (189.91 s) |
73.36 (15.00 s) |
68.92 (109.09 s) |
66.05 (0.20 s) |
| SW1SW2CSHPZV | 74.41 (278.47 s) |
74.41 (17.47 s) |
66.31 (117.14 s) |
69.19 (0.25 s) |
| SW1SW2CSHPZVL1 | 73.10 (404.82 s) |
73.32 (49.41 s) |
66.84 (101.01 s) |
68.66 (0.25 s) |
| SW1SW2CSHPZVL1L4 | 72.84 (576.29 s) |
72.06 (57.83 s) |
67.10 (107.88 s) |
67.62 (0.25 s) |
| SW1SW2CSHPZVL1L4L14 | 72.58 (748.72 s) |
72.36 (19.43 s) |
66.84 (163.85 s) |
69.19 (0.28 s) |
| SW1SW2CSHPZVL1L4L14L30 | 73.36 (811.54 s) |
71.01 (83.93 s) |
66.57 (197.57 s) |
68.40 (0.31 s) |
Test set accuracy of sequentially adding fold discriminatory data sources (continued) according to the ranking of kernel weights obtained by MKLdiv-dc over all data sources. The results of the Bayesian kernel learning method were not employed in [21], hence we do not list in the table. The term inside the parenthesis is the CPU running time (seconds). The best test set accuracy of each kernel learning method is marked in bold.