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Noise reduction by multi-target learning

We review the problems associated with neural networks learning from noisy and/or ambiguous training data and propose a simple procedure that appears to alleviate these problems. By making use of the readily available output error information, a network is able to choose the correct output targets from sets of possibilities and generate new targets if any of the correct ones appear to be missing from the given training data.