Skip to main content
. 2018 May 2;13(5):e0196527. doi: 10.1371/journal.pone.0196527

Fig 6. Comparison of elastic-forward BIC selection versus oracle lasso under active learning sampling.

Fig 6

Whisker plot of performance indicators as a function of number of samples; elastic-forward BIC selection is shown in red, oracle lasso in blue. The whisker plot is obtained from 10 independent trials. External stimuli are drawn from the recommended Active Learning distribution; network SW1CL has a total of 24 non-zero parameters out 864 potential regressors, while network SW3CL has a total of 58 non-zero parameters out of 4536 potential regressors. The elastic-forward BIC selection still outperforms the oracle lasso for larger sample sizes. Both methods show a performance gain on the samples drawn from the Active Learning distribution when compared to their uniform sampling counterparts.