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Cluster analysis was applied to classify the forest types.
The model was applied to classify CCSD-MH metamorphic rocks and get the cluster centers of each class.
In this study a Q-mode hierarchical cluster analysis (HCA) was applied to classify samples according to their parameters.
A counter propagation artificial neural network (CP-ANN) was applied to classify the estrogen receptor selectivity of 94 benzopyrans.
Based on the performance matrix, 'K-means' Clustering method (centre = 2) was applied to classify participants as the good or bad navigators.
The Naive Bayes algorithm was applied to classify textual sentiment, which was used as the basis for constructing the indicator for the degree of divergence.
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In the output layer of the network, the softmax classifier is applied to classify samples.
Secondly, the Softmax classifier is applied to classify the precipitation patterns, including rain, snow, and hail, and it is based on the features extracted by deep learning algorithm.
Moreover, the Softmax classifier is applied to classify the precipitation pattern such as rain, snow, and hail based on the features extracted by the convolution neural network algorithm.
Then if RotBoost ensemble classifier is applied to classify such dataset directly, a rotation matrix with thousands of dimensions is required for each tree, which greatly increases the computational complexity.
Finally, the quadratic discriminant classifier described in Section 2.5 is applied to classify protein sequences.
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