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Chen et al. [13] compared the SVM-based method with multilayer perceptrons (MLP) based on text verification over four independent features, namely, the distance map feature, the gray-scale spatial derivative feature, the constant gradient variance feature and the DCT coefficient feature.
The Difference Variance feature is a measure of variation in image contrast.
With exception of the Variance feature all were calculated after standardization of the time series to variance 1.
For example, the variance feature (the second row in Table 1) could be computed according to the following (modified) expression: (3) f 2 = ∑ x ∈ Ω s x 2 − 4 μ Ω ∑ x ∈ Ω s x + 4 μ Ω 2 N Ω + ∑ x ∈ Ω d x 2 2 N Ω.
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The Pure Variance Common Features model is a factor model for the conditional variances of a portfolio of assets, designed to isolate a small number of variance features that drive all assets' volatilities.
The descriptor is finally obtained by concatenating the distribution matrices in a sequence determined by entropy and variance features of the projections.
An empirical example provides evidence that models with very few variance features perform well in capturing the long-run common volatilities of the equity components of the Dow Jones.
The experimental results illustrate that the proposed multi-sensor system can achieve an overall recognition accuracy of 96.4% by adopting the mean and variance features, using the Decision Tree classifier.
In [20], LBP variance features provide really promising results.
Based on these observations, we trained the SVM classifiers either with only WP features or with the combination of Mel Scale, AR model error, and spectrum variance features.
We note that the best separation performance was obtained with the combination of Mel Scale, AR model error, and spectrum variance features on the dataset SR2 (AUC = 0.483).
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