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Sources of support: This work was supported by the Research Council of Norway (grant #191431/V50 to AKR) and the Norwegian Cancer Society (grant #421852 to GMM).
This work was supported by the Norwegian Cancer Society (C99026 to IG and GMM) and The National Program for Research in Functional Genomics of the Research Council of Norway (158954/S10 to GMM).
This work was supported by the Research Council of Norway (grant #191431/V50 to AKR and grant #193375/V50 to GB) and the Norwegian Cancer Society (grant #421852 to GMM).
MLP model performs significantly better relative to GMM in various AV dimensions.
The final results are reported according to GMM second order considering the efficiency that it brings to the estimates.
Monte Carlo studies show that the performance of this GMM estimator for bivariate and trivariate models is similar to GMM estimation for univariate multifractal models.
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The performance of MLP-AVSS is again superior to GMM-AVSS as is predicted.
The superiority of MLP-AVSS relative to GMM-AVSS is consistent with classification accuracies of MLP and GMM-based AV models presented in Section 5.1.
Under mismatched/noisy conditions, more modest relative improvements in both validity (up to 41.5%) and reliability (up to 12.1%) were obtained relative to GMM-SVM results.
On both male and female databases under studio-clean conditions, substantial improvements from the least squares configuration relative to GMM-UBM baseline were observed.
Relative separation performance of methods: In lower mixture sizes (N=2,3), MLP-AVSS method provides higher output SIRs relative to GMM-AVSS and both of them are superior to JADE-AV for alpha-digits corpus.
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