Regularization and error bars for the mixture of experts network

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 1, Issue , 1997, Pages 221-225

  Ramamurti, Viswanath ^a   Ghosh, Joydeep ^a  

^a The University of Texas at Austin   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CRITICAL APPLICATIONS; DATA DISTRIBUTION; FUNCTION APPROXIMATION; GENERALIZATION PERFORMANCE; MIXTURE OF EXPERTS; MIXTURE OF EXPERTS NETWORK; MODULAR APPROACH; NETWORK PREDICTION;

ESTIMATION; NEURAL NETWORKS; APPROXIMATION THEORY; COMPUTER SIMULATION; EXPERT SYSTEMS; PARAMETER ESTIMATION;

MIXTURES; NEURAL NETWORKS;

ERROR BARS; EXPERT ARCHITECTURE; FUNCTION APPROXIMATION; GATING NETWORKS; REGULARIZATION;

EID: 0030703285     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNN.1997.611668     Document Type: Conference Paper

References (13)

1. C. G. Atkeson, A. W. Moore, and S. Schaal. Locally weighted learning. Technical report, submitted to Artificial Intelligence Review, May 1996. http://www. cc. gatech. edu/fac/Chris. Atkeson.
2. A. Ben-Israel and T. N. E. Greville. Generalized Inverses: Theory and Applications. Belmont, CA: Wadsworth International Group, 1984.
3. C. M. Bishop. Improving the generalization properties of radial basis function neural networks. Neural Computation, 3(4):579-588, 1991.
4. J. S. Denker and Y. leCun. Transforming neural-net output levels to probability distributions. In R. P. Lippmann, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 3, pages 853-859. Morgan Kaufmann, 1991.
5. D. Harrison and D. L. Rubinfeld. Hedonic prices and the demand for clean air. J. Environ. Economics and Management, 5331-102, 1978. http://lib. stat. cmu. edu/datasets/boston.
6. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton. Adaptive mixtures of local experts. Neural Computation, 3:79-87, 1991.
7. M. I. Jordan and R. A. Jacobs. Hierarchical mixture of experts and the EM algorithm. Neural Computation
8. J. Leonard, M. Kramer, and L. Ungar. Using radial basis functions to approximate a function and its error bounds. ieeenn, 3:4:624-627, July 1992.
9. D. J. C. MacKay. A practical framework for backpropagation networks. Neural Computation, 4(3):448-472, May 1992.
10. V. Ramamurti and J. Ghosh. Advances in using hierarchical mixture of experts for signal classification. In Proceedings of ICASSP 96, pages 3569-3572, 1996.
11. V. Ramamurti and J. Ghosh. Structural adaptation inmixture of experts. In Proceedings of IC,PR 96, Track D, pages 704-708, 1996.
12. A. S. Weigend, B. A. Huberman, and D. Rumelhart. Predicting the future: A connectionist approach. International Journal of Neural Systems, 1:193-209, 1991.
13. L. Xu, M. I. Jordan, and G. E. Hinton. Pm alternative model for mixture of experts. In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Adviunces in Neural Information Processing Systems 7, pages 633-640. The MIT Press, 1995.