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In study of EEG signal classification problems, an important factor in improving the recognition rate is to extract representative features to represent the EEG signal properly.
Pathway-based classification can overcome these challenges by using robust, aggregate features to represent biological mechanisms.
The first challenge concerns difficulty with identifying significant term features to represent original content by considering the hidden knowledge.
One of the main problems in pattern recognition is obtaining the best set of features to represent the data.
We extract rock color and texture features and using mutual information we selected 14 from 36 features to represent the data in a lower dimensional space.
In order to capture the dynamic characteristics of facial events, we design the dynamic haar-like features to represent the temporal variations of facial appearance.
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We interpret this damp and sinuous feature to represent the trace of the Ghaggar–Hakra palaeo-drainage system.
"Westfront" was hailed as the first German talking picture to concern the war as well as the first German feature to represent life in the trenches.
First, we utilize the affinity propagation clustering technology to design a codebook, and then construct a soft coding feature to represent the whole dynamic texture sequence.
We then employ the more adaptive feature to represent the virtual region.
Olivieri et al. [12] proposed a spatiotemporal motion feature to represent activities termed motion vector flow instance (MVFI) templates.
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