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- 1
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Geoffrey Hinton and
Drew van Camp.
Keeping neural networks simple by minimizing the description length
of the weights.
In Proceedings of the COLT'93, pages 5-13, Santa Cruz,
California, 1993.
- 2
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Éric Moulines,
Jean-François Cardoso, and
Elisabeth Gassiat.
Maximum likelihood for blind separation and deconvolution of noisy
signals using mixture models.
In Proceedings of the ICASSP'97, pages 3617-3620, Munich,
Germany, 1997.
- 3
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David J. C. MacKay.
Developments in probabilistic modelling with neural
networks--ensemble learning.
In Neural Networks: Artificial Intelligence and Industrial
Applications. Proceedings of the 3rd Annual Symposium on Neural Networks,
Nijmegen, Netherlands, 14-15 September 1995, pages 191-198, Berlin, 1995.
Springer.
- 4
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Lawrence K. Saul,
Tommi Jaakkola, and
Michael I. Jordan.
Mean field theory for sigmoid belief networks.
Journal of Artificial Intelligence Research, 4:61-76, 1996.
- 5
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David J. C. MacKay.
Comparison of approximate methods for handling hyperparameters.
Neural Computation.
Submitted.
- 6
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David J. C. MacKay.
Ensemble learning for hidden Markov models.
Available from http://wol.ra.phy.cam.ac.uk/, 1997.
- 7
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David Barber and
Christopher M. Bishop.
Ensemble learning for multi-layer networks.
In M. I. Jordan, M. J. Kearns, and S. A. Solla, editors,
Advances in Neural Information Processing Systems 10, pages 395-401.
MIT Press, 1998.
- 8
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David Barber and
Bernhard Schottky.
Radial basis functions: a bayesian treatement.
In M. I. Jordan, M. J. Kearns, and S. A. Solla, editors,
Advances in Neural Information Processing Systems 10, pages 402-408.
MIT Press, 1998.
- 9
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Christopher M. Bishop,
Neil Lawrence,
Tommi Jaakkola, and
Michael I. Jordan.
Approximating posterior distributions in belief networks using
mixtures.
In M. I. Jordan, M. J. Kearns, and S. A. Solla, editors,
Advances in Neural Information Processing Systems 10, pages 416-422.
MIT Press, 1998.
- 10
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Harri Lappalainen.
Ensemble learning for unsupervised neural networks.
Technical report, Helsinki University of Technology, Laboratory of
Computer and Information Science, 1998.
In preparation.
- 11
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Hagai Attias.
Independent factor analysis.
Neural Computation, 11:803-851, 1999.
Harri Lappalainen
7/10/1998