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Artificial neural network (ANN) is a powerful tool for reservoir characterization; it can be used for classifications of categorical reservoir variables, as well as for predictions of continuous reservoir properties.
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This structure can also be used for classification purposes.
SVM can be used for classification, regression and other tasks.
SVMs is a supervised machine learning method which can be used for classification or regression analysis.
It can be used for classification and prediction based on experiences.
This feature can be used for classification and identification of BOC-modulated signals with unknown parameters.
K neighbor algorithm can not only be used for classification but also can be used for regression analysis.
The preliminary analysis suggests that the features can be used for classification with Gaussian Mixture Models (GMM).
The resulting feature vectors y k ∈ R m × 1, with k = 1,⋯,N, can then be used for classification.
The SVMs numerically finds the distinguishing features of a particular class which may be used for classification.
After trying different parameter values the one that yields the highest Jaccard index should be used for classification.
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