On the infeasibility of training neural networks with small squared errors

Advances in Neural Information Processing Systems

Volumn , Issue , 1998, Pages 371-377

  Vu, Van H ^a  

^a YALE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

THREE TERM CONTROL SYSTEMS;

CLASS-F; DATA SET; INPUT VECTOR; NP-HARD; RELATIVE ERRORS; SQUARED ERRORS;

NEURAL NETWORKS;

EID: 0005811345     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. S. Arora and C. Lund Hardness of approximation, book chapter, preprint.
2. A. Blum, R. Rivest Training a 3-node neural network is NP-hard Neutral Networks, Vol 5., p 117-127, 1992.
3. A. Blumer, A. Ehrenfeucht, D. Haussler, M. Warmuth, Learnabiliiy and the Vepnik-Chervonenkis Dimension, Journal of the Association for computing Machinery, Vol 36, No. 4, 929-965, 1989.
4. M. Garey and D. Johnson, Computers and intractability: A guide to the theory of NP-completeness, Sail Francisco, W.H.Freeman, 1979.
5. D. Haussler, Generalizing the PAC model for neural net and other learning applications (Tech. Rep. UCSC-CRL-89-30). Santa Cruz. CA: University of California 1989.
6. L. Jones, The computational intractability of training sigmoidal neural networks (preprint)
7. J. Judd Neutral Networks and Complexity of learning, MIT Press 1990.
8. N. Meggido, On the complexity of polyhedral separability (Tech. Rep. RJ 5252) IBM Almaden Research Center, San Jose. CA.
9. V. H. Vu, On the infeasibility of training neural networks with small squared error, manuscript.