Journal of Optimization / 2016 / Article / Tab 1

Research Article

Evidence Maximization Technique for Training of Elastic Nets

Table 1

Elastic nets trained with several fixed regularization parameters and .

Sparseness (%)Mean log likelihoodError (%)

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

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