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Developments in Probabilistic Modelling with Neural Networks — Ensemble Learning

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Neural Networks: Artificial Intelligence and Industrial Applications

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.

    MATH  Google Scholar 

  • Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S. (1995) The Helmholtz machine. Neural Computation. to appear.

    Google Scholar 

  • Feynman, R. P. (1972) Statistical Mechanics. W. A. Benjamin, Inc.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • MacKay, D. J. C. (1992) Bayesian interpolation. Neural Computation 4 (3): 415–447.

    Google Scholar 

  • MacKay, D. J. C., (1995a) Ensemble learning and evidence maximization. submitted to NIPS*95.

    Google Scholar 

  • MacKay, D. J. C. (1995b) Free energy minimization algorithm for decoding and cryptanalysis. Electronics Letters 31 (6): 446–447.

    Article  Google Scholar 

  • Neal, R. M., and Hinton, G. E. (1993) A new view of the EM algorithm that justifies incremental and other variants. Biometrika. submitted.

    Google Scholar 

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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

  • Online ISBN: 978-1-4471-3087-1

  • eBook Packages: Springer Book Archive

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