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In the first stage, we used the high-dimension CWT-F0 features for prosody features training.
In these studies, different biological evidences are extracted and used as features training the classifier, including Gene Ontology (GO) functional annotations [ 8, 9], protein sequences [ 10] and co-expressed proteins [ 11].
After features training, the decision threshold could be obtained and traffic congestion was carried out.
Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features, training examples, or both.
The constraint acts as a guidance for features training to concentrate on non-occluded facial parts.
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We use Haar-like features trained by an adaboost algorithm to get a colorless representation of the ball.
It first extracts dark areas in full high resolution image, then detects new impact craters within dark areas using a cascade classifier which combines local binary pattern features and Haar-like features trained by an AdaBoost machine learning algorithm.
Table 4 Classification rates for data, using features trained with data.
Due to the constraint, features trained by NMF algorithm become more spatially localized and converge faster into expected facial regions.
Since local features trained by classifiers can effectively cancel out the problems caused by semantic gap, which leads to an overall significant improvement of the classification performance [37 39].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com