A hybrid projection-based and radial basis function architecture: Initial values and global optimisation

Pattern Analysis and Applications

Volumn 5, Issue 2, 2002, Pages 113-120

  Cohen, S ^a   Intrator, N ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

Clustering; Hybrid network architecture; Projection units; RBF units; Regularisation; SMLP

Indexed keywords

EID: 0036083110     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s100440200010     Document Type: Article

References (34)

1. Bishop CM. Improving the generalization properties of radial basis function neural networks. Neural Computation, 1991; 3(4):579-588
2. Flake GW. Square unit augmented, radially extended, multilayer percpetrons. In: Orr GB, Müller K (eds), Neural Networks: Tricks of the Trade, Spring-Verlag, 1998; 145-163
3. Deterding DH. Speaker Normalisation for Automatic Speech Recognition. PhD thesis, University of Cambridge, 1989
4. Donoho DL, Johnstone IM. Projection-based approximation and a duality with kernel methods. Annals of Statistics 1989; 17: 58-106
5. Schetzen M. The Volterra and Wiener Theories Of Nonlinear Systems. Wiley, New York, 1980
6. Volterra V. Theory of Functional and of Integro-differential Equations. Dover, 1959
7. Hastie T, Tibshirani R. Generalized additive models. Statistical Science 1986; 1: 297-318
8. Hastie T, Tibshirani R. Generalized Additive Models. Chapman & Hall, London, 1990
9. Stone CJ. The dimensionality reduction principle for generalized additive models. Ann Statistics 1986; 14: 590-606
10. Orr MJ, Hallam J, Murray A, Leonard T. Assessing RBF networks using DELVE. IJNS, 2000
11. Lee YC, Doolen G, Chen HH, Sun GZ, Maxwell T, Lee HY, Giles CL. Machine learning using higher order correlation networks. Physica D 1986; 22-D: 276-306
12. Cohen S, Intrator N. A hybrid projection based and radial basis function architecture. In: Kittler J, Roli F (eds), Proc Int Workshop on Multiple Classifier Systems (LNCS1857), Sordinia, Italy. Springr-Verlag 2000; 147-156
13. Duda RO, Hart PE. Pattern Classification and Scene Analysis. Wiley, New York, 1973
14. Orr MJ. Introduction to Radial Basis Function Networks. Technical report, Institute for Adaptive and Neural Computation, Edinburgh University, 1996 (http://www.anc.ed.ac.uk/~mjo/rbf.html)
15. Noether GE. Elements of Non-Paramteric Statistics. Wiley, New York, 1967
16. Fukunaga K. Introduction to Statistical Pattern Recognition. Academic Press, London, 1990
17. Stuart A, Kendall M, Ord JK. Advanced Theory of Statistics 1: Distribution Theory. Oxford University Press, 1987
18. Friedman JH, Tukey JW. A projection pursuit algorithm for exploratory data analysis. IEEE Trans Computers 1974; C(23): 881-889
19. Friedman JH. Exploratory projection pursuit. J Am Statistical Assoc 1987; 82: 249-266
20. Intrator N, Cooper LN. Objective function formulation of the BCM theory of visual cortical plasticity: Statistical connections, stability conditions. Neural Networks 1992; 5: 3-17
21. Cohen S, Intrator N. Global optimization of RBF networks. IEEE Trans Neurd Networks (2000 submitted)
22. Wang Z, Zhu T. An efficient learning algorithm for improving generalization performance of radial basis function neural networks. Neural Neworks 2000; 13(4,5)
23. Orr MJL, et al. Combining regression trees and radial basis functions. Int J Neural Systems 2000; 10(6): 453-466
24. Wasserman PD. Advanced Methods in Neural Computing. Van Nostrand Reinhold, New York, 1993
25. Bishop CM. Neural Networks for Pattern Recognition. Oxford University Press, 1995
26. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. Proceedings Royal Statistical Society 1977; B-39: 1-38
27. MacKay DJC. Bayesian interpolation. Neural Computation 1992; 4(3): 415-447
28. Friedman JH. Mutltivariate adaptive regression splines. Ann Statistics 1991; 19: 1-141
29. Gorman RP, Sejnowski TJ. Analysis of hidden units in a layered network trained to classify sonar targets. Neural Network 1988; 1: 75-89
30. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds), Parallel Distributed Processing, volume 1, MIT Press, Cambridge, MA, 1986; 318-362
31. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. The Wadsworth Statistics/Probability Series, Belmont, CA, 1984
32. Hastie T, Tibshirani R, Buja A. Flexible discriminant analysis by optimal scoring. J Am Statistical Assoc 1994; 89: 1255-1270
33. Buckheit J, Donoho DL. Improved linear discrimination using time-frequency dictionaries. Technical Report, Stanford University, 1995
34. Robinson AJ. Dynamic Error Propogation Networks. PhD thesis, University of Cambridge, 1989