A constructive cascade network with adaptive regularisation

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

Volumn 1607, Issue , 1999, Pages 40-49

  Treadgold, N K ^a   Gedeon, T D ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; NEURAL NETWORKS;

ADAPTIVE METHODS; CASCADE ALGORITHM; CASCADE CORRELATION; CASCADE NETWORKS; GENERALISATION; REGULARISATION;

FEEDFORWARD NEURAL NETWORKS;

EID: 84958054961     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/BFb0100470     Document Type: Conference Paper

References (11)

1. N. K. Treadgold and T. D. Gedeon, “A cascade network algorithm employing progressive rprop,” in Proc. of the Int. Work-Conf. on Artificial and Natural Neural Systems, Lanzarote, Spain, June 1997, pp. 723-732.
2. N. K. Treadgold and T. D. Gedeon, “Extending casper: A regression survey,” in Proc. of the Int. Conf. on Neural Information Processing, Dunedin, New Zealand, Nov. 1997, pp. 310-313.
3. S. E. Fahlman and C. Lebiere, “The Cascade-Correlation learning architecture,” in Advances in Neural Information Processing Systems 2, D. S. Touretzky, Ed. San Mateo, CA: Morgan Kaufmann, 1990, pp. 524-532.
4. L. Prechelt, “Probenl - a set of neural network benchmark problems and benchmarking rules,” Tech. Rep. 21/94, Fakultät für Informatik, Universität Kahlsruhe, 1994.
5. M. Riedmiller and H. Braun, “A direct adaptive method for faster backpropagation learning: The RPROP algorithm,” in Proc. of the IEEE Int. Conf. on Neural Networks, San Francisco, CA, Apr. 1993, pp. 586-591.
6. N. K. Treadgold and T. D. Gedeon, “Simulated annealing and weight decay in adaptive learning: The sarprop algorithm,” IEEE Transactions on Neural Networks, vol. 9, pp. 662-668, July 1998.
7. J. S. Bridle, “Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition,” in Neuro-computing: Algorithms, Architectures and Applications, F: Fogelman Soulid and J. Hérault, Eds. Berlin: Springer-Verlag, 1990, pp. 227-236
8. C. Bishop, Neural Networks for Pattern Recognition. Oxford: Oxford University Press, 1995.
9. J.-N. Hwang, S.-R. Lay, M. Maechler, R. D. Martin, and J. Schimert, “Regression modeling in back-propagation and projection pursuit learning,” IEEE Transactions on Neural Networks, vol. 5, pp. 342-353, May 1994.
10. R. Steel and J. Torrie, Principles and Procedures of Statistics A Biomedical Approach. Singapore: McGraw-Hill, 1980.
11. L. Prechelt, “Investigation of the cascor family of learning algorithms,” Neural Networks, vol. 10, no. 5, ppo 885-896, 1997.