Abstract
Feature selection is an important phase in pattern recognition. It has also an important role in neural network methods even though it is often neglected. The fundamental function of feature selection is to find a set of features that will represent the pattern vector in a most optimal way. Only the information that is either redundant or irrelevant to the classification task is removed from the pattern vectors. The dimension of the feature vector is usually smaller than the dimension of the pattern vector so the computation time and the memory requirements are greatly reduced.
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