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The network topological features evaluated using r values related to power law distribution indicate that WHPN is a typical scale-free network.
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Furthermore, the predictive power of several audio features is evaluated using a new multi-label feature selection method.
In this approach, features are evaluated using an SVM classifier built during the feature search, and there is a 5-fold cross-validation on the training database.
Features were evaluated using Ranker and Info Gain Attribute Evaluation algorithm, and multiple classifiers were investigated to recognize the beats.
In this work, various methods that utilize spectral and/or spatial features were evaluated using a hyperspectral face image dataset.
The six different sets of features are evaluated using a time-frequency footprint with the same number of neighbor frequencies and time frames, Nfreqs = Nframes.
The proposed ranking method may also be applied to other time and/or frequency domain features and evaluated using other robust classifiers such as SVM and MLP.
The importance of each of these features is evaluated using a bag of decision trees and the results are discussed in a latter section.
Clinicopathologic features were evaluated using the Pearson Chi-Square test.
Comparisons among the clinical and pathological features were evaluated using χ and Fisher's exact tests.
The association between R72P genotype and disease features was evaluated using the Fisher's exact test.
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