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Figure 10 Quality performances with sequential feature selection.
Startle modulation during the feature was larger in the sequential feature positive group than in the sequential feature negative group.
One group received sequential feature positive training (X → A+/A−), and a second group was trained in a sequential feature negative design (X → A−/A+).
Initially, some feature selection methods (such as NCA, LASSO and sequential feature selections) were investigated.
The first experiment (N = 18) employed a within-subjects, sequential feature positive design (X → A+/A−).
Then, we forwarded all these samples to the sequential feature selection (SFS) method for selecting the best features.
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We estimate the performance of GE relative to three alternative methods of selecting or combining features that distinguish an epileptic gamma (∼65 95 Hz) oscillation from normal activity: forward sequential feature-selection, backward sequential feature-selection, and genetic programming.
Finally, a long short-term memory receives these sequential features in proper order to memorize and recognize the sequential patterns.
By modeling the low-dimensional sequential features, we can achieve motion recognition, which is confirmed by experiments.
For a protein sequence, we extract six kinds of sequential features, which will be fed into the SVR classifier.
How BSKs activate downstream proteins such as BSU1, as well as the structural consequences of their unusual sequential features, was unclear.
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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