Sparse solution in training artificial neural networks
Multilayer perceptrons (MLPs) for non-linear regression within a common framework with support vector machines (SVMs) and radial basis function regularised networks for non-linear regression are presented. The aim is taking advantage of the SVMs training paradigm to overcome the curse of dimensionality and too strict hypothesis on the statistics of errors in traditional MLPs for non-linear regression. In this context, an alternative strategy to quadratic programming, based on 1-norm minimisation to avoid computational problems of SVMs, is proposed.
artificial neural networks multilayer perceptrons support vector machines radial-basis function regularised networks