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SVM analysis based on reactivity in the parietal lobe did not yield significant classifications.
Along with these thresholds one could establish prognostically significant classifications indicating when a patient with knee pain after TKA should be revised.
For functional scans, significant classifications were obtained both at whole brain level and when restricting the analysis to the fear network while gray matter SVM analyses correctly classified participants only when using the whole brain search volume.
SVM analyses using either functional or structural imaging both yielded significant classifications with accuracies comparable to studies on other psychiatric disorders [18] indicating that SVM is a feasible method to discriminate between SAD and HC.
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In the SVM classification, the whole multivariate VBM pattern over the set of thresholded areas jointly generated the significant classification results, and the significance of such results therefore refers to the whole pattern.
A candidate pattern is selected for creating a new unit only if it produces a significant quadratic error while presenting a significant classification potential from its neighborhood properties.
Overall results confirmed that even a simple combination approach provided a significant classification score improvement over the best performing single-resolution DNN.
The comparison details show that ACOFS has a remarkable ability to generate reduced-size subsets of salient features while yielding significant classification accuracy.
Acoustic analysis of the calls used as stimuli showed no sex-, age- or familiarity-specific acoustic cues, but highly significant classification results at the individual level.
The within-condition results replicated previous findings showing significant classification accuracy reduction when there was less attention compared with more attention.
This did not give statistically significant classification.
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