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In the classic text classification framework, a feature extraction mechanism extracts features from raw articles, including all distinct terms (words).
It then extracts features from one set versus another.
The recognition module extracts features from these traces and feeds them to an artificial neural network (ANN).
To do so, the system initially extracts features from these images.
The method first extracts features from the CAD model of a complex part.
The system extracts features from voice recordings and considers an advanced statistical approach for pattern recognition.
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After the feature extraction stage, the extracted features are fed into Gaussian mixture model (GMM) classifiers.
Two distinctive processes are involved in keystroke dynamics: feature extraction and classification of the extracted features.
Many existing feature extraction methods can be utilized to extract features for image magnification problem.
The pose matrix from extracted features was calculated by Homography.
So the software must extract features from the shaking data that are frame independent.
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