A hybridization of Bayesian and neural learning

Neurocomputing

Volumn 48, Issue 1-4, 2002, Pages 443-453

  Abdelbar, Ashraf M ^a   Attia, Sahar ^a   Tagliarini, Gene A ^b  

^a The American University in Cairo   (Egypt)

^b UNIVERSITY OF NORTH CAROLINA WILMINGTON   (United States)

Author keywords

Bayesian methods; High order networks; Hybrid methods; Learning

Indexed keywords

BACKPROPAGATION; LEARNING SYSTEMS; POLYNOMIALS; STATISTICAL METHODS;

BAYESIAN LEARNING;

NEURAL NETWORKS;

ACCURACY; ANALYTIC METHOD; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CLASSIFICATION; CONTROLLED STUDY; ERROR; HYBRID COMPUTER; INTERMETHOD COMPARISON; LEARNING; MATHEMATICAL COMPUTING; MULTIVARIATE ANALYSIS; PRIORITY JOURNAL; PROCESS OPTIMIZATION; REVIEW; STATISTICAL ANALYSIS;

EID: 0036825895     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(01)00608-7     Document Type: Review

References (11)

1. A.M. Abdelbar, Achieving superior generalization with a high order neural network, Neural Comput. Appl. 7 (1998) 141-146.
2. A.M. Abdelbar, G.A. Tagliarini, Using neural network learning in an Othello evaluation Function, Journal of Exp. Theor. Artif Intell. 10 (1998) 217-230.
3. R. Duda, P. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973.
4. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice-Hall, Upper Saddle River, NJ, 1999.
5. A.G. Ivakhnenko, Polynomial theory of complex systems, IEEE Trans. Syst. Man Cybern. 12 (1971) 364-378.
6. K.-F. Lee, S. Mahajan, A pattern classification approach to evaluation function learning, Artif. Intell. 36 (1988) 1-25.
7. K.-F. Lee, S. Mahajan, The development of a world class Othello program, Artif. Intell. 43 (1990) 21-36.
8. P.M. Murphy, D.W. Aha, UCI Repository of Machine Learning Databases, University of California, Irvine, Department of Information and Computer Science, http://www.ics.uci.edu/ ~mlearn/.
9. S. Narayan, ExpoNet: a generalization of the multi-layer perceptron model, Proceedings World Congress on Neural Networks, vol. 3, 1993, pp. 494-497.
10. D.E. Rumelhart, G.E. Hinton, J.L. McClelland, A general framework for parallel distributed processing, in: D.E. Rumelhart, J.L. McClelland, and the PDP Research Group (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Bradford, Cambridge, 1986.
11. T.Y. Young, T.W. Calvert, Classification, Estimation and Pattern Recognition, American Elsevier Publishing Company, New York, 1974.