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

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.<>