On the approximation rate of hierarchical mixtures-of-experts for generalized linear models

Neural Computation

Volumn 11, Issue 5, 1999, Pages 1183-1198

  Jiang, Wenxin ^a   Tanner, Martin A ^a  

^a NORTHWESTERN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; NERVE CELL NETWORK; THEORETICAL MODEL;

ALGORITHMS; MODELS, THEORETICAL; NERVE NET;

EID: 0033161418     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976699300016403     Document Type: Article

References (7)

1. Jacobs, R. A., Jordan, M. I. Nowlan, S. J., & Hinton, G. E. (1991). Adaptive mixtures of local experts. Neural Comp., 3, 79-87.
2. Jordan, M. I., & Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Comp., 6, 181-214.
3. Jordan, M. I., & Xu, L. (1995). Convergence results for the EM approach to mixtures-of-experts architectures. Neural Networks, 8, 1409-1431.
4. McCullagh, P., & Nelder, J. A. (1989). Generalized linear models. London: Chapman and Hall.
5. Mhaskar, H. N. (1996). Neural networks for optimal approximation of smooth and analytic functions. Neural Comp., 8, 164-177.
6. Peng, F., Jacobs, R. A., & Tanner, M. A. (1996). Bayesian inference in mixtures-of-experts and hierarchical mixtures-of-experts models with an application to speech recognition. Journal of American Statistical Association, 91, 953-960.
7. Zeevi, A., Meir, R., & Maiorov, V. (1998). Error bounds for functional approximation and estimation using mixtures of experts. IEEE Trans. Information Theory, 44, 1010-1025.