Kernel Regression and Backpropagation Training With Noise

Abstract

One method proposed for improving the generalization capability of a feedforward network trained with the backpropagation algorithm is to use artificial training vectors which are obtained by adding noise to the original training vectors. We discuss the connection of such backpropagation training with noise to kernel density and kernel regression estimation… (More)

Topics

6 Figures and Tables

Slides referencing similar topics