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Ulaş, M. Semerci, O.T. Yıldız, E. Alpaydın, Incremental construction of classifier and discriminant ensembles, Information Sciences, 179 (9) (2009) 1298 1318] and has two parts: first, we investigate the effect of four factors on correlation: (i) algorithms used for training, (ii) hyperparameters of the algorithms, (iii) resampled training sets, (iv) input feature subsets.
These systems implicitly performed the spectral analysis of correlation (i.e. symmetric positive definite) matrices.
Blue: voxels with negative correlation, i.e. [18F]FDG accumulation decreases with increasing depletion severity.
Blue: voxels with negative correlation, i.e. metabolism decreases with increasing lesion severity.
Red: voxels with positive correlation, i.e. metabolism increases with increasing lesion severity.
Random rankings show no correlation (i.e., ρ close to 0), as expected.
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Significant correlations (i.e., correlations with p-value < 0.05) across phones are illustrated in Figure 3 for HAMD total and sub-topic ratings.
However, crossover points indicate that the same correlations (i.e. temporal behaviour) do not extend across the whole sequence.
Notably, the LNL fits become almost perfect for lower correlations i.e. (rho= 0.05) (data not shown).
The exponents obtained for Re and Sc in Sh were 0.43 and 0.39, values comparable with those found in literature correlations, i.e., 0.33.
The mean-field approximation (MFA) cannot account for such local correlations (i.e. ordered adlayers), whereas the quasi-chemical approximation gives results quite close to the Monte Carlo simulations.
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