Smoothing regularizers for projective basis function networks

Advances in Neural Information Processing Systems

Volumn , Issue , 1997, Pages 585-591

  Moody, John E ^a   Rögnvaldsson, Thorsteinn S ^a  

^a OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

RADIAL BASIS FUNCTION NETWORKS;

BASIS FUNCTION NETWORKS; BASIS FUNCTIONS; GENERALIZATION ERROR; INPUT AND OUTPUTS; MONTE CARLO INTEGRATION; NETWORK WEIGHTS; ORDER SMOOTHING; RADIAL BASIS FUNCTIONS;

FUNCTIONS;

EID: 84899007626     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Bishop, C. (1993), 'Curvature-driven smoothing: A learning algorithm for feedforward networks', IEEE Trans. Neural Networks 4, 882-884.
2. Eubank, R. L. (1988), Spline Smoothing and Nonparametric Regression, Marcel Dekker, Inc.
3. Friedman, J. H. & Stuetzle, W. (1981), "Projection pursuit regression" Amer. Stat. Assoc. 76(376), 817-823.
4. Geman, S., Bienenstock, E. & Doursat, R. (1992), 'Neural networks and the bias/variance dilemma'. Neural Computation 4(1), 1-58.
5. Girosi, F., Jones, M. & Poggio, T. (1995), 'Régularisation theory and neural network architectures', Neural Computation 7,219-269.
6. Hastie, T. J. & Tibshirani, R. J. (1990), Generalized Additive Models, Vol. 43 of Monographs on Statistics and Applied Probability, Chapman and Hall.
7. Moody, J. E. & Yarvin, N. (1992), Networks with learned unit response functions, in J. E. Moody, S. J. Hanson & R. P. Lippmann, eds, Advances in Neural Information Processing Systems 4", Morgan Kaufmann Publishers, San Mateo, CA, pp. 1048-55.
8. Moody, J. & Rögnvaldsson, T. (1996), Smoothing regularizers for projective basis function networks, Submitted to Neural Computation.
9. Moody, J. & Rögnvaldsson, T. (1997), Smoothing regularizers for radial basis function networks, Manuscript in preparation
10. Poggio, T. & Girosi, F. (1990), 'Networks for approximation and learning", IEEE Proceedings 78(9).
11. Powell, M. (1987), Radial basis functions for multivariable interpolation: a review., in J. Mason & M. Cox, eds, 'Algorithms for Approximation', Clarendon Press, Oxford.
12. Wahba, G. (1990), Spline models for observational data, CBMS-NSF Regional Conference Series in Applied Mathematics.