Abstract
Ensembles of neural networks is a technique that uses several different networks working together to find a relationship that exactly matches a many-to-one training set for digital classification problems. Each network learns a different region of the training space and all these regions fit together, like pieces of a jigsaw puzzle, to cover the entire training space. The individual networks are ‘grown’ as they are needed to form either cascades or branches of networks. The networks can be of any type such as backpropagation, cascade etc. However, virtually any other technique can be used in place of the networks: GA, EA, DRS, tabu search, nearest neighbour, and so on. Methods are discussed to improve the generalisation.
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References
T.C. Fogaxty. First nearest neighbour classification on Frey and Slates’ letter recognition problem. Machine Learning, 9:387–388, 1992.
P.W. Frey and D.J. Slate. Letter recognition using Holland-style adaptive classifiers. Machine Learning, 6:161–182.
J.R. Podlena and T. Hendtlass. Evolving complex neural networks that age. In Proc. ICEC95. IEEE Press, 1995.
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© 1998 Springer-Verlag Wien
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Philpot, D., Hendtlass, T. (1998). Ensembles of Neural Networks for Digital Problems. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_7
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_7
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive