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Admissibility of Memorization Learning with Respect to Projection Learning in the Presence of Noise
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1999/02/25
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Bio-Cybernetics and Neurocomputing
feedforward neural network, generalization, training error, over-learning, admissibility,
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In learning of feed-forward neural networks, so-called 'training error' is often minimized. This is, however, not related to the generalization capability which is one of the major goals in the learning. It can be interpreted as a substitute for another learning which considers the generalization capability. Admissibility is a concept to discuss whether a learning can be a substitute for another learning. In this paper, we discuss the case where the learning which minimizes a training error is used as a substitute for the projection learning, which considers the generalization capability, in the presence of noise. Moreover, we give a method for choosing a training set which satisfies the admissibility.