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Note that this chapter uses the term incremental learning to denote data selection, and should not be confused with the NN architecture selection growing approach. The term AW growing is used in this chapter to denote the process of finding an optimal architecture starting with too few hidden units and adding units during training. In contrast to incremental learning, selective learning selects at each selection interval a new training subset from the original candidate set. Selected patterns are not removed from the candidate set. At each selection interval, all candidate patterns have a chance to be selected. The subset is selected and used for training until some convergence criteria on the subset is met (e.g. a specified error limit on the subset is reached, the error decrease per iteration is too small, the maximum number of epochs allowed on the subset is exceeded). 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