Bayesian ARTMAP for regression
Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
10-2013
Abstract
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification.
The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons.
Recommended Citation
Sasu, L. M., & Andonie, R. (2013). Bayesian ARTMAP for regression. Neural Networks, 46, 23–31. https://doi.org/10.1016/j.neunet.2013.04.006
Journal
Neural Networks
Rights
Copyright © 2013 Elsevier Ltd. All rights reserved.
Comments
This article was originally published in Neural Networks. The full-text article from the publisher can be found here.
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