Mathematical Problems in Engineering

Research Article

Efficient Model Selection for Sparse Least-Square SVMs

Table 7

Training time (in CPU seconds) of the FLSA-SVM, the RFLSA-SVM, the SVMs, and the LS-SVM on the image benchmark.
FLSA-SVMs RFLSA-SVMs log2( )SVMs LS-SVMs λ = 0.0135
λ = 2−5λ = 2−4λ = 2−3SMO CG
1 1.0940 0.0000 0.9060 9.8130 1.2326
65 10.2810 0.1400 0.9060 5.9070 1.2312
130 10.2340 0.3750 0.9060 3.7820 1.3601
195 10.0160 0.6560 0.9220 2.5630 1.5557
260 9.7500 0.8750 0.8910 1.8440 1.8066
325 9.4540 1.3280 0.7970 1.2970 2.0622
390 9.0780 1.4220 0.6720 0.9840 2.3108
455 8.7040 1.6720 0.5780 0.7810 2.9718
520 8.3750 2.0470 0.4840 0.6090 3.2617
585 7.9210 2.3280 0.4530 0.5310 3.9884
650 7.4070 2.6880 0.4220 0.4680 4.7408
715 6.8290 2.9220 0.4060 0.4530 5.8988
780 6.2350 3.0620 0.4530 0.4370 7.9950
845 5.5940 3.4690 0.4530 0.4540 10.4827
910 4.8910 3.8280 0.4220 0.4530 14.0951
975 4.0930 4.1720 0.4530 0.4370 18.2100
1040 3.3750 4.3590 0.4690 0.3910 24.0141
1105 2.5470 4.7970 0.5160 0.4070 32.1312
1170 1.7350 4.8900 0.5940 0.4220 39.7957
1235 0.5620 5.2340 0.5470 0.4220 54.6313
1300 0.0000 0.0000 0.6720 0.4220 76.5178
= 65 11.3750 0.1400  NA NA NA
= 260 41.3750 2.0460  NA NA NA
= 1300 47.5460 26.2070  19.7540 8.3890 105.6486