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Comments on "Noise injection into inputs in back propagation learning"

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2 Author(s)
Grandvalet, Y. ; Centre de Recherches de Royallieu, Univ. de Technol. de Compiegne, France ; Canu, S.

The generalization capacity of neural networks learning from examples is important. Several authors showed experimentally that training a neural network with noise injected inputs could improve its generalization abilities. In the original paper (ibid., vol. 22, no. 3. p. 436-40, 1992), Matsuoka explained this fact in a formal way, claiming that using noise injected inputs is equivalent to reduce the sensitivity of the network. However, the author states that an error in Matsuoka's calculations lead him to inadequate conclusions. This paper corrects these calculations and conclusions.<>

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:25 ,  Issue: 4 )

Date of Publication:

April 1995

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