Efficient BP algorithms for general feedforward Neural Networks

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

Volumn 4527 LNCS, Issue PART 1, 2007, Pages 327-336

  Espana Boquera S ^a   Zamora Martinez F ^b   Castro Bleda, M J ^a   Gorbe Moya, J ^a  

^a UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

^b UNIVERSITAT JAUME I   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BACKPROPAGATION; FEATURE EXTRACTION; PROBLEM SOLVING; TOPOLOGY;

CONSECUTIVE RETRIEVAL PROBLEM; TOOLKIT FACILITATES;

FEEDFORWARD NEURAL NETWORKS;

EID: 38149089513     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-73053-8_33     Document Type: Conference Paper

References (14)

1. C. M. Bishop. Neural networks for pattern recognition. Oxford University Press, 1996.
2. J. Bridle. Neuro-computing: Algorithms, Architectures, and Applications., chapter Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition, pages 227-236. Springer-Verlag, 1989.
3. Michael R. Garey and David S. Johnson. Computers and Intractability. A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, 1979.
4. Sakti P. Ghosh. File organization: the consecutive retrieval property. Commun. ACM, 15(9):802-808, 1972.
5. Babak Hassibi and David G. Stork. Second order derivatives for network pruning: Optimal brain surgeon. In Advances in NIPS, volume 5, pages 164-171. Morgan Kaufmann, 1993.
6. Roberto Ierusalimschy. Programming in Lua. Published by Lua.org, December 2003.
7. L. T. Kou. Polynomial complete consecutive information retrieval problems. SIAM J. Comput., 6:67-75, 1977.
8. Y. LeCun, J. Denker, S. Solla, R. E. Howard, and L. D. Jackel. Optimal brain damage. In Advances in NIPS II. Morgan Kauffman, 1990.
9. Nicholas Nethercote and Jeremy Fitzhardinge. Valgrind: A Program Supervision Framework. Electronic Notes in Theoretical Computer Science, 89(2), 2003.
10. D. Plaut, S. Nowlan, and G. Hinton. Experiment on learning by back propagation. Technical Report CMU-CS-86-126, Department of Computer Science, Carnegie Mellon University, 1986.
11. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. PDP: Computational models of cognition and perception, I, chapter Learning internal representations by error propagation, pages 319-362. MIT Press, 1986.
12. P. Werbos. Backpropagation: Past and future. In Proceedings of the IEEE International Conference on Neural Networks, pages 343-353. IEEE Press, 1988.
13. Francisco Zamora Martínez. Implementación eficiente del algoritmo de retropropagación del error con momentum para redes hacia delante generales, 2005. Proyecto Final de Carrera.
14. Andreas Zell, Niels Mache, Ralf Huebner, Michael Schmalzl, Tilman Sommer, and Thomas Korb. SNNS: Stuttgart Neural Network Simulator. User manual Version 4.1. Technical report, Stuttgart, 1995.