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The GLR PNN is derived from the original PNN by incorporating a fully connected recurrent layer between the pattern and output layers.
Prediction performance was optimized with variations in three training factors, the number of pattern units, width of radial basis function, and initial weight distribution between the pattern and output layers.
The goal is then to compute the number of matches of the length-M pattern with all length-M substrings of the length-N text, i.e., to compute a vector of N−M+1 scores, where the ith score is the number of matches between the pattern and the substring that begins at text position i.
These examples show that there is a strong correlation between the pattern and the rhythm of breathing and a person's emotional state, or more generally, state of mind.
Immunoblots revealed no significant differences between the pattern and degree of binding in these two strains.
Moreover, d̃ (P, x i) is the generalized distance between the pattern and its best instance in the i-th sequence.
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Moreover, surface potential microscopy experiments also exhibited a clear contrast between the patterned and non-patterned regions.
When the correspondence between the patterns and the images are determined, the depth data is calculated according to the projector-camera geometric relationship.
Future studies should especially focus on interactions between the patterns and (negative) synergy effects of multiple patterns in individual patients.
Factor loadings indicated the strength and direction of the association between the patterns and food groups.
We introduce the input parameter m as the number of mismatches allowed between the patterns and their CR.
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