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number of training patterns.
There are a fixed number of training patterns in the training file.
The second one acts to reduce the number of training patterns involved in the design process.
Selection of training patterns was accomplished by the user after viewing a graphic display of the EEG signal.
Additionally, it does not require the communication of training patterns, which is a crucial component in realistic big data implementations.
It incorporates terms representing positive and negative correlation of pairs of training patterns with respect to class labels.
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When applied to the set of train patterns, (hbox {Info}(S)) measures the average amount of information needed to identify the class of the pattern (S).
General possibility of increasing training speed (in term of consumed training patterns) of autoencoders is confirmed.
Until the firing strengths of all of the training patterns satisfy a pre-specified threshold, the process of rule generation is terminated.
The second novelty consists of the introduction of a new criterion for the removal of tough training patterns that generate the problem of over-fitting.
When this parameter is unity, the number of categories equals the number of unique training patterns, thus memorizing the training set.
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