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Time-resolved multivariate classification of target type.
Time-resolved multivariate classification of target location.
Sample sites were allocated based on a multivariate classification of landscape attributes.
We used category-based (faces vs. scenes) multivariate classification of electroencephalography signals to determine if individual target items were successfully retrieved or suppressed.
Multivariate classification of the data using SVM at the whole-brain level revealed the best discrimination accuracy for all three groups using FDG-PET, with 81% (chance level 33%), in comparison to GM and WM information, with lowest accuracy using WM information on its own (Table 3).
The multivariate classification of the discovered biomarkers, thus, stabilized the prediction and allowed for differential diagnosis.
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Recently, a multivariate classification study of cortical thickness demonstrated 82% sensitivity and 85% specificity for differentiating AD from DLB [ 43].
These approaches could easily spoil one of the main advantages of a multivariate classification method such as SVMs, as prefiltering of features by common univariate methods (such as the t test) might remove genes that do not reach significance when tested individually but provide useful information when taken together with other, correlated variables.
Another example of a 'high information' analysis approach is the use of multivariate classification.
The application of multivariate classification techniques on fMRI data has been shown effective in multiple studies, e.g. (LaConte et al. 2005, 2007; Sitaram et al. 2011).
For this reason the simultaneous quantification of multiple biomarkers, that may include demographic/risk-factor(s), cervical length and biochemical marker(s), and the development of multivariate classification models represent a promising approach to improving diagnostic efficiency.
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