Best approximation by Heaviside perceptron networks

In Lp-spaces with p [1, ) there exists a best approximation mapping to the set of functions computable by Heaviside perceptron networks with n hidden units; however for p (1, ) such best approximation is not unique and cannot be continuous. Keywords. One-hidden-layer networks, Heaviside perceptrons, best approximation, metric projection, continuous selection, approximatively compact.

• Type: Journal
• Year: 2000
• Where: NN
• Authors: Paul C. Kainen, Vera Kurková, Andrew Vogt
• Keywords: Heaviside Perceptron, Metric Projection, Neural Networks, NN 2000, One-hidden-layer Networks