ACMD: A practical tool for automatic neural net based learning

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

Volumn 2199, Issue , 2001, Pages 168-173

  Linder, Roland ^a   Pöppl, Siegfried J ^a  

^a UNIVERSITY OF LÜBECK   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA HANDLING; INFORMATION ANALYSIS;

APPROXIMATION PROBLEMS; FINE TUNING; LEARNING PARAMETERS; MEDICAL CLASSIFICATION; MEDICAL DATA; NET-BASED LEARNING; NEURAL NETWORK APPLICATION;

MEDICAL PROBLEMS;

EID: 23044530434     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45497-7_25     Document Type: Conference Paper

References (11)

1. Rumelhart, D. E., Hinton, G. E., & Williams, R. J.: Learning Representations by Back-Propagating Errors. Nature 323 (1986) 533-536
2. Orr, G. B., Müller, K.-R. (eds.): Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science, Vol. 1524. Springer-Verlag, Berlin Heidelberg New York (1998)
3. Kwok, T.Y., Yeung, D.Y.: Constructive algorithms for structure learning in feed forward neural networks for regression problems. IEEE Trans. on Neural Networks 8(3) (1997) 630-645
4. Ash, T.: Dynamic node creation in backpropagation networks. Connection Science 1(4) (1989) 365-375
5. Fahlman, S.E., Lebiere, C.: The cascade-correlation learning architecture. In: Touretzky, D.S. (ed.): Advances in Neural Information Processing Systems 2, Morgan Kaufmann, CA (1990) 524-532
6. Anand, R., Mehrotra, K., Mohan, C.K., Ranka, S.: Efficient Classification for Multiclass Problems Using Modular Neural Networks. IEEE Trans. on Neural Networks. 6(1) (1995) 117-124
7. Linder, R., Wirtz, S., Pöppl, S.J.: Speeding up Backpropagation Learning by the APROP Algorithm. Second International ICSC Symposium on Neural Computation, Proceedings CD, Berlin (2000).
8. Prechelt, L.: Proben 1. a set of neural network benchmark problems and benchmarking rules. Technical Report 21/94, Fakultät für Informatik, Universität Karlsruhe. Available via ftp.ira.uka.de in directory /pub/papers/techreports/1994 as file 1994-21.ps.Z. (1994)
9. Linder, R., Pöppl, S.J.: Backprop, RPROP, APROP: Searching for the best learning rule for an electronic nose. Neural Networks in Applications’99. In: Proc. of the Fourth International Workshop, Magdeburg, Germany, (1999) 69-74
10. Ng, S.C., Leung, S.H., Lik, A.: Fast Convergent Generalized Back-Propagation Algorithm with Constant Learning Rate. Neural Processing Letters 9 (1999) 13-23
11. Schraudolph, N.N.: A Fast, Compact Approximation of the Exponential Function. Neural Computation 11 (1999) 853-862