Abstract
Ensemble learning by variational free energy minimization is a framework for statistical inference in which an ensemble of parameter vectors is optimized rather than a single parameter vector. The ensemble approximates the posterior probability distribution of the parameters.
In this paper I give a review of ensemble learning using a simple example.
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References
Box, G. E. P., and Tiao, G. C. (1973) Bayesian inference in statistical analysis. Addison—Wesley.
Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S. (1995) The Helmholtz machine. Neural Computation. to appear.
Feynman, R. P. (1972) Statistical Mechanics. W. A. Benjamin, Inc.
Hinton, G. E., and van Camp, D., (1993) Keeping neural networks simple by minimizing the description length of the weights. In: Proceedings of COLT-93.
Hinton, G. E., and Zemel, R. S. (1994) Autoencoders, minimum description length and Helmholtz free energy. In Advances in Neural Information Processing Systems 6, ed. by J. D. Cowan, G. Tesauro, and J. Alspector, San Mateo, California. Morgan Kaufmann.
MacKay, D. J. C. (1992) Bayesian interpolation. Neural Computation 4 (3): 415–447.
MacKay, D. J. C., (1995a) Ensemble learning and evidence maximization. submitted to NIPS*95.
MacKay, D. J. C. (1995b) Free energy minimization algorithm for decoding and cryptanalysis. Electronics Letters 31 (6): 446–447.
Neal, R. M., and Hinton, G. E. (1993) A new view of the EM algorithm that justifies incremental and other variants. Biometrika. submitted.
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© 1995 Springer-Verlag London Limited
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MacKay, D.J.C. (1995). Developments in Probabilistic Modelling with Neural Networks — Ensemble Learning. In: Kappen, B., Gielen, S. (eds) Neural Networks: Artificial Intelligence and Industrial Applications. Springer, London. https://doi.org/10.1007/978-1-4471-3087-1_37
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DOI: https://doi.org/10.1007/978-1-4471-3087-1_37
Publisher Name: Springer, London
Print ISBN: 978-3-540-19992-2
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