On virtually binary nature of probabilistic neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1451, Issue , 1998, Pages 765-774

  Grim, Jiří ^a   Pudil, Pavel ^a  

^a INSTITUTE OF INFORMATION THEORY AND AUTOMATION   (Czech Republic)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION MAKING; FEEDFORWARD NEURAL NETWORKS; MIXTURES; MULTILAYER NEURAL NETWORKS; MULTILAYERS; NEURONS; PATTERN RECOGNITION;

APPROXIMATION ACCURACY; FINITE DISTRIBUTION; INTERCONNECTION STRUCTURE; MULTI-DIMENSIONAL SPACE; MULTILAYER FEEDFORWARD NEURAL NETWORKS; PROBABILISTIC NEURAL NETWORKS; SEQUENTIAL DESIGN; STATISTICAL DECISION-MAKING;

PROBABILITY DISTRIBUTIONS;

EID: 84947724177     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0033301     Document Type: Conference Paper

References (13)

1. Bishop C.M.: Neural Networks for Pattern Recognition. Clarendon Press: Oxford, (1995)
2. Dempster A.P., Laird N.M., Rubin D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B 39 (1977) 1-38
3. Grim J.: On numerical evaluation of maximum likelihood estimates for finite mixtures of distributions. Kybernetika 18 (1982) 173-190
4. Grim J.: Multivariate statistical pattern recognition with nonreduced dimensionality. Kybernetika 22 (1986) 142-157
5. Grim J.: Maximum Likelihood Design of Layered Neural Networks. Proceedings of the 13th International Conference on Pattern Recognition IV, Los Alamitos: IEEE Computer Society Press (1996) 85-89
6. Grim J.: Design of multilayer neural networks by information preserving transforms. Proceedings of the Third European Congress on System Science, Eds. E. Pessa, M.P. Penna, A. Montesanto, Roma: Edizzioni Kappa (1996) 977-982
7. Grim J.: Maximum-Likelihood Structuring of Probabilistic Neural Networks. Research Report UTIA, AS CR, No. 1894, (1997)
8. Haykin S.: Neural Networks: a comprehensive foundation. Morgan Kaufman: San Mateo CA, (1993)
9. Palm H.Ch.: A new method for generating statistical classifiers assuming liamar mixtures of Gaussian densities. Proceedings of the 12th IAPR International Conference on Pattern Recognition, Jerusalem II, Los Alamitos: IEEE Computer Society Press (1994) 483-486
10. Schlesinger M.I.: Relation between learning and self-learning in pattern recognition (in Russian). Kibernetika (Kiev), No. 2 (1968) 81-88
11. Specht D.F.: Probabilistic neural networks for classification, mapping or associative memory. Proceeding of the IEEE Int. Conference on Neural Networks, July 1988, 1 (1988) 525-532
12. Streit L.R., Luginbuhl T.E.: Maximum likelihood training of probabilistic neural networks. IEEE Trans. on Neural Networks 5 (1994) 764-783
13. Vajda I., Grim J.: About optimality of probabilistic basis function neural networks. Research Report UTIA, AS CR, No. 1887, (1996)