Hinging hyperplanes for regression, classification, and function approximation

Abstract

A hinge function y = h (x) consists of two hyperplanes continuously joined together at a hinge. In regression (prediction), classification (pattern recognition), and noiseless function approximation, use of sums of hinge functions gives a powerful and efficient alternative to neural networks with compute times several orders of magnitude less than fitting neural networks with a comparable number of parameters. The core of the methodology is a simple and effective method for finding good hinges.