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Noise injection into inputs in sparsely connected Hopfield and winner-take-all neural networks

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1 Author(s)
L. Wang ; Sch. of Comput. & Math., Deakin Univ., Clayton, Vic., Australia

In this paper, we show that noise injection into inputs in unsupervised learning neural networks does not improve their performance as it does in supervised learning neural networks. Specifically, we show that training noise degrades the classification ability of a sparsely connected version of the Hopfield neural network, whereas the performance of a sparsely connected winner-take-all neural network does not depend on the injected training noise

Published in:

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)  (Volume:27 ,  Issue: 5 )