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FReM – scalable and stable decoding with fast regularized ensemble of models

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Academic year: 2021

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Figure

Figure 1: Illustration of nested cross-validation: Two cross- cross-validation loops are run one inside the other
Figure 2: Regularized ensemble of models: Fast regularized ensemble of models uses one loop less than bagging.
Figure 3: Comparison of the performance of FReM and bagging: Comparison on two discriminative tasks, discrimination of famous and scrambled faces from the Henson (2006) dataset, and discrimination of response  inhibi-tion on openfMRI ds009 (Poldrack et al.
Figure 4: Relative performance: Relative prediction accuracy, weight stability and computation time for different classification tasks.
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