Optimal Linear Combinations of Neural Networks

 HASHEM Sherif
 Pacific Northwest National Laboratory
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Author(s)

 HASHEM Sherif
 Pacific Northwest National Laboratory
Journal

 Neural Networks

Neural Networks 10(4), 599614, 19970601
References: 57

1
 Multiple networks for function learning

ALPAYDIN E.
Proceedings of the 1993 IEEE International Conference on Neural Networks 1, 914, 1993
Cited by (1)

2
 Conditioning Diagnostics

BELSLEY D. A.
Collinearity and Weak Data in Regression, 1991
Cited by (1)

3
 Regression Diagnostics

BELSLEY D. A.
Identifying Influential Data and Sources of Collinearity, 1980
Cited by (1)

4
 Parallel consensual neural networks

BENEDIKTSSON J. A.
Proceedings of the 1993 IEEE International Conference on Neural Networks 1, 2732, 1993
Cited by (1)

5
 <no title>

BREIMAN L.
Stacked regressions, 1992
Cited by (1)

6
 Forecasting with more than one model

BUNN D. W.
Journal of Forecasting 8, 161166, 1989
Cited by (1)

7
 Pragmatic comparison of statistical and neural network methods for function estimation

CHERKASSKY V.
Proceedings of the 1995 World Congress on Neural Networks 2, 917926, 1995
Cited by (1)

8
 Linear constraints and the efficiency of combined forecasts

CLEMEN R. T.
Journal of Forecasting 5, 3138, 1986
Cited by (1)

9
 Hybrid neural network architectures. Equilibrium systems that pay attention

COOPER L.
Neural Networks: Theory and Applications, 8196, 1991
Cited by (1)

10
 Combining forecasts  twenty years later

GRANGER C. W. J.
Journal of Forecasting 8, 167173, 1989
Cited by (1)

11
 Improved methods of combining forecasts

GRANGER C. W. J.
Journal of Forecasting 3, 197204, 1984
Cited by (1)

12
 Collinearity and the use of latent root regression for combining GNP forecasts

GUERARD J. B. Jr.
Journal of Forecasting 8, 231238, 1989
Cited by (1)

13
 Optimal Linear Combinations of Neural Networks

HASHEM S.
PhD thesis, School of Industrial Engineering, Purdue University
Cited by (1)

14
 Approximating a function and its derivatives using MSEoptimal linear combinations of trained feedforward neural networks

HASHEM S.
Proceedings of the 1993 World Congress on Neural Networks 1, 617620, 1993
Cited by (1)

15
 An efficient model for product allocation using optimal combinations of neural networks

HASHEM S.
Intelligent Engineering Systems through Artificial Neural Networks 3, 669674, 1993
Cited by (1)

16
 <no title>

HAYKIN S.
Neural NetworksA Comprehensive Foundation, 1994
Cited by (67)

17
 <no title>

HINES W. W.
Probability and Statistics in Engineering and Management Science, 1990
Cited by (1)

18
 A competitive modular connectionist architecture

JACOBS R. A.
Advances in Neural Information Processing System 3, 767773, 1991
Cited by (1)

19
 Hierarchical mixtures of experts and the EM algorithm

JACOBS R. A.
Neural Computation 6, 181214, 1994
Cited by (1)

20
 Back propagation is sensitive to initial conditions

KOLEN J. F.
Advaces in Neural Information Processing Systems 3, 860867, 1991
Cited by (1)

21
 <no title>

LAPLACE P. D.
Deuxieme Supplement a la Theorie Analytique des Probabilites
Cited by (1)

22
 Projection pursuit learning networks for regression

MAECHLER M.
Proceedings of the 2nd International Conference on Tools for Artificial Intelligence, 350358, 1990
Cited by (1)

23
 Specification of predictive distribution from a combination of forecasts

MENEZES L.
Methods of Operations Research 64, 397405, 1991
Cited by (1)

24
 Generalization and parameter estimation in feedforward nets: Some experiments

MORGAN N.
Advances in Neural Information Processing Systems 2, 630637, 1990
Cited by (2)

25
 <no title>

MOSKOWITZ H.
Statistics for management and economics, 1985
Cited by (1)

26
 Improving the generalising capabilities of a backpropagation network

NAMATAME A.
The International Journal of Neural Networks Research and Applications 1(2), 8694, 1989
Cited by (1)

27
 <no title>

NETER J.
Applied linear statistical models, 1985
Cited by (3)

28
 Hierarchical neural networks for partial diagnosis in medicine

OHNOMACHADO L.
Proceedings of the 1994 World Congress on Neural Networks 1, 291296, 1994
Cited by (1)

29
 Neural network classifier for hepatoma detection

PARMANTO B.
Proceedings of the 1994 World Congress on Neural Networks 1, 285290, 1994
Cited by (1)

30
 ChaitinKolmogorov complexity and generalization in neural networks

PEARLMUTTER B. A.
Advances in neural information processing systems 3, 925931, 1991
Cited by (2)

31
 Improving Regression Estimation: Averaging Methods for Variance Reduction with Extensions to General Convex Measure Optimization

PERRONE M. P.
PhD thesis, Department of Physics, Brown University
Cited by (1)

32
 When networks disagree: Ensemble methods for hybrid neural networks

PERRONE M. P.
Neural networks for speech and image processing, 1993
Cited by (2)

33
 <no title>

SCHEAFFER R. L.
Probability and Statistics for Engineers, 1990
Cited by (2)

34
 <no title>

SOBOL' I. M.
The Monte Carlo method, 1974
Cited by (1)

35
 Generalization by weightelimination with application to forecasting

WEIGEND A. S.
Advances in Neural Information Processing Systems 3, 875882, 1991
Cited by (9)

36
 <no title>

ZURADA J. M.
Introduction to Artificial Neural Systems, 1992
Cited by (14)

37
 Democracy in neural nets : voting schemes for classification

BATTITI R.
Neural Networks 7(4), 691707, 1994
DOI Cited by (8)

38
 Improving the accuracy of an artificial neural network using multiple deffirently trained networks

BAXT W. G.
Neural Computation 4(5), 772780, 1992
DOI Cited by (2)

39
 Constrained topological mapping for nonparametric regression analysis

CHERKASSKY V.
NeuralNetworks 2, 2740, 1991
Cited by (6)

40
 Combining forecasts : A review and annotated bibliography

CLEMEN R. T.
J. Forecasting 5, 559583, 1989
DOI Cited by (3)

41
 Combining economic forecasts

CLEMEN R. T.
Journal of Business and Economic Statistics 4(1), 3946, 1986
DOI Cited by (1)

42
 Statistically controlled activation weight intialization (SCAWI)

DRAGO G. P.
IEEE Trans. Neural Networks 3(4), 627631, 1992
Cited by (3)

43
 Double backpropagation increasing generalization performance

DRUCKER H.
Proceedings of the 1991 International Joint Conference on Neural Networks in Seattle 2, 145150, 1991
Cited by (1)

44
 <no title>

GARDNER E. S. Jr.
The future of forecasting. International Journal of Forecasting 4, 325330, 1988
DOI Cited by (1)

45
 Neural Network Ensemble

HANSEN L. K.
IEEE Trans. Pattern Analysis and Machine Intelligence 12(10), 9931001, 1990
DOI Cited by (26)

46
 Improving model accuracy using optimal linear combinations of trained neural networks

HASHEM S.
IEEE Transactions on Neural Networks 6(3), 792794, 1995
Cited by (1)

47
 Optimal linear combinations of neural networks: An overview

HASHEM S.
Proceedings of the 1994 IEEE International Conference on Neural Networks 3, 15071512, 1994
Cited by (1)

48
 Unbiasedness, efficiency and the combination of economic forecasts

HOLDEN K.
Journal of Forecasting 8, 175188, 1989
DOI Cited by (1)

49
 Regression modeling in backpropagation and projection pursuit learning

HWANG J. N.
IEEE Trans. Neural Netw. 5(3), 342353, 1994
Cited by (10)

50
 Subpopulation policies for a parallel multiobjective genetic algorithm with applications to wing design

FUJII T.
Proc. International Conference on Systems, Man, and Cybernetics, 31423147, 1998
DOI Cited by (5)

51
 A statistical approaches to learning and generalization in layered neural networks.

LEVIN E.
Proceedings of IEEE 78(10), 15681674, 1990
DOI Cited by (31)

52
 Lowering variance of decisions by using artificial neural networks portfolios

MANI G.
Neural Computation 3, 484486, 1991
DOI Cited by (1)

53
 Portfolio selection

MARKOWITZ H.
Journal of Finance 7(1), 7791, 1952
DOI Cited by (8)

54
 Combining the results of several neural network classifiers

ROGOVA G.
Neural Networks 7(5), 777781, 1994
Cited by (1)

55
 Latent root regression analysis

WEBSTER J. T.
Technometrics 16(4), 513522, 1974
DOI Cited by (1)

56
 Sensitivity of weights in combining forecasts

WINKLER R. L.
Operations Research 40(3), 609614, 1992
Cited by (1)

57
 Stacked Generalization

WOLPERT D. H.
Neural Networks 5, 241259, 1992
Cited by (25)
Cited by: 2

1
 Clustering ensembles of neural network models

BAKKER Bart , HESKES Tom
Neural networks : the official journal of the International Neural Network Society 16(2), 261269, 20030301
References (23)

2
 New training strategies for constructive neural networks with application to regression problems

MA L. , KHORASANI K.
Neural Networks 17(4), 589609, 200405
References (36) Cited by (2)