Journal article
A mixed-integer programming approach to GRNN parameter estimation
Information Sciences, Vol.320, pp.1-11
2015
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.
Details
- Title
- A mixed-integer programming approach to GRNN parameter estimation
- Authors/Creators
- G.E. Lee (Author/Creator) - Murdoch UniversityA. Zaknich (Author/Creator) - Murdoch University
- Publication Details
- Information Sciences, Vol.320, pp.1-11
- Publisher
- Elsevier Inc.
- Identifiers
- 991005542864607891
- Copyright
- © 2015 Elsevier Inc
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.61 Artificial Intelligence & Machine Learning
- 4.61.493 Neural-Fuzzy Integration
- Web Of Science research areas
- Computer Science, Information Systems
- ESI research areas
- Computer Science