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A mixed-integer programming approach to GRNN parameter estimation

Lee, G.E. and Zaknich, A. (2015) A mixed-integer programming approach to GRNN parameter estimation. Information Sciences, 320 . pp. 1-11.

Link to Published Version: http://dx.doi.org/10.1016/j.ins.2015.04.052
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Abstract

A mixed-integer programming formulation for sparse general regression neural networks (GRNNs) is presented, along with a method for estimating GRNN parameters based on techniques drawn from support vector machines (SVMs) and evolutionary computation. GRNNs have been widely used for regression estimation, learning a function from a set of input/output examples, but they utilise the full set of training examples to evaluate the interpolation function. Sparse GRNNs choose a subset of the training examples, analogous to the support vectors chosen by SVMs. Experimental comparisons are made with non-sparse GRNNs and with sparse GRNNs whose centres are randomly chosen or are chosen using vector quantisation of the input domain. It is shown that the mixed-integer programming approach leads to lower prediction errors compared with previous approaches, especially when using a small fraction of the training examples.

Item Type: Journal Article
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Elsevier Inc.
Copyright: © 2015 Elsevier Inc
URI: http://researchrepository.murdoch.edu.au/id/eprint/27986
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