Kernel Regression and Backpropagation Training With Noise

Part of Advances in Neural Information Processing Systems 4 (NIPS 1991)

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Petri Koistinen, Lasse Holmström


One method proposed for improving the generalization capability of a feed(cid:173) forward network trained with the backpropagation algorithm is to use artificial training vectors which are obtained by adding noise to the orig(cid:173) inal training vectors. We discuss the connection of such backpropagation training with noise to kernel density and kernel regression estimation. We compare by simulated examples (1) backpropagation, (2) backpropagation with noise, and (3) kernel regression in mapping estimation and pattern classification contexts.