Evidence Maximization Technique for Training of Elastic Nets

Table 1

Elastic nets trained with several fixed regularization parameters and .

		Sparseness (%)	Mean log likelihood	Error (%)
0	0	2.46 ± 0.00	0.0638 ± 0.0007	2.06 ± 0.05
3		10.32 ± 1.56	0.0583 ± 0.0011	1.85 ± 0.05
10		16.84 ± 2.49	0.0609 ± 0.0007	1.81 ± 0.06
30		45.87 ± 4.03	0.0823 ± 0.0005	2.18 ± 0.05
100		63.26 ± 2.90	0.1419 ± 0.0003	3.41 ± 0.05
300		75.77 ± 4.37	0.2503 ± 0.0004	5.19 ± 0.05
1	1	3.75 ± 0.16	0.0621 ± 0.0007	2.00 ± 0.04
1	10	3.81 ± 0.19	0.0621 ± 0.0007	2.00 ± 0.04
1	30	6.71 ± 2.54	0.0607 ± 0.0013	1.95 ± 0.07
10	1	16.73 ± 2.40	0.0609 ± 0.0007	1.81 ± 0.06
10	10	16.56 ± 2.46	0.0613 ± 0.0007	1.81 ± 0.06
10	30	16.25 ± 2.53	0.0621 ± 0.0006	1.82 ± 0.05
10	100	16.20 ± 3.09	0.0649 ± 0.0005	1.86 ± 0.05
30	100	38.42 ± 2.42	0.0862 ± 0.0005	2.20 ± 0.05
100	30	61.51 ± 2.45	0.1428 ± 0.0003	3.41 ± 0.05
100	100	59.18 ± 2.09	0.1445 ± 0.0003	3.41 ± 0.05
0	1	2.46 ± 0.00	0.0638 ± 0.0007	2.06 ± 0.05
	10	2.46 ± 0.00	0.0638 ± 0.0007	2.06 ± 0.04
	100	2.46 ± 0.00	0.0638 ± 0.0007	2.05 ± 0.04
	300	2.46 ± 0.00	0.0659 ± 0.0006	2.05 ± 0.06