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References

1
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
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
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
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
David J. C. MacKay.
Comparison of approximate methods for handling hyperparameters.
Neural Computation.
Submitted.

6
David J. C. MacKay.
Ensemble learning for hidden Markov models.
Available from http://wol.ra.phy.cam.ac.uk/, 1997.

7
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
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
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
Harri Lappalainen.
Ensemble learning for unsupervised neural networks.
Technical report, Helsinki University of Technology, Laboratory of Computer and Information Science, 1998.
In preparation.

11
Hagai Attias.
Independent factor analysis.
Neural Computation, 11:803-851, 1999.



Harri Lappalainen
7/10/1998