Active training of backpropagation neural networks using the learning by experimentation methodology

Annals of Operations Research

Volumn 75, Issue , 1997, Pages 105-122

  Lin, Fu Ren ^a   Shaw, Michael J ^b  

^a NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Active learning; Learning by experimentation methodology; Neural networks

Indexed keywords

EID: 0031325886     PISSN: 02545330     EISSN: 15729338     Source Type: Journal    
DOI: 10.1023/a:1018999110972     Document Type: Article

References (22)

1. D. Cohn, L. Atlas and R. Ladner, Improving generalization with active learning, Machine Learning 15(1994)201-221.
2. P.A. Devijer and J. Kittler, Pattern Recognition: A Statistical Approach, Prentice-Hall, London, 1982.
3. R.C. Eberhart, The role of genetic algorithms in neural network query-based learning and explanation facilities, in: COGANN-92, 1992, pp. 169-182.
4. S. Fahlman, An empirical study of learning speed in a back-propagation network, Technical Report CMU-CS-88-162, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 1988.
5. S. Fahlman, Faster-learning variations on back-propagation: An empirical study, in: Proceedings of the 1988 Connectionist Models Summer School, Morgan Kaufmann, Los Altos, CA, 1988.
6. J.A. Freeman and D.M. Skapura, Neural Networks: Algorithms, Application, and Programming Technology, Addison-Wesley, 1992.
7. R.F. Gorman and T.J. Sejnowski, Analysis of hidden units in a layered network trained to classify sonar targets, Neural Networks 1(1988)75-89.
8. J-N. Hwang, S-O. Choi and R.J. Marks, Query-based learning applied to partially trained multilayer perceptrons, IEEE Transaction on Neural Networks 2(1991)131-136.
9. R. Kerber, Chimerge: Discretization of numeric attributes, in: 11th National Conference on Artificial Intelligence, 1992, pp. 123-128.
10. D. Kulkarni and H.A. Simon, Experimentation in machine discovery, in: Computational Models of Scientific Discovery and Theory Formulation, J. Shranger and P. Langley, eds., Morgan Kaufmann, San Mateo, CA, 1990, pp. 255-273.
11. P. Langley, G.L. Bradshaw and H.A. Simon, Rediscovering chemistry with the bacon system, in: Machine Learning: An Artificial Intelligence Approach, R.S. Michalski, J.G. Carbonell and T.M. Mitchell, eds., Morgan Kaufmann, Los Altos, CA, 1983, pp. 307-330.
12. D.B. Lenat, The role of heuristics in learning by discovery: Three case studies, in: Machine Learning: An Artificial Intelligence Approach, R.S. Michalski, J.G. Carbonell and T.M. Mitchell, eds., Morgan Kaufmann, Los Altos, CA, 1983, pp. 243-306.
13. R. Lippman, An introduction to computing with neural nets, IEEE ASSP Magazine, April 1987, pp. 4-22.
14. T.M. Mitchell, P.E. Utgoff and R. Banerji, Learning by experimentation: Acquiring and refining problem-solving heuristics, in: Machine Learning: An Artificial Intelligence Approach, R.S. Michalski, J.G. Carbonell and T.M. Mitchell, eds., Morgan Kaufmann, Los Altos, CA, 1983, pp. 163-190.
15. B. Nordhausen and P. Langley, An integrated approach to empirical discovery, in: Computational Models of Scientific Discovery and Theory Formulation, J. Shranger and P. Langley, eds., Morgan Kaufmann, San Mateo, CA, 1990, pp. 97-128.
16. S. Piramuthu, N. Raman and M.J. Shaw, Learning-based scheduling in a manufacturing flexible flow line, Technical Report, Beckman Institute, University of Illinois at Urbana-Champaign, 1993.
17. L. Rendell and H. Cho, Empirical learning as a function of concept character, Machine Learning 5(1990)267-298.
18. D. Rumelhart, G. Hinton and J. McClelland, A general framework for parallel distributed processing, in: Parallel Distributed Processing, Explorations in the Microstructure of Cognition, Vol. 1, The MIT Press, Cambridge, MA, 1987, Chap. 2.
19. S.C. Park, N. Raman and M.J. Shaw, Intelligent scheduling with machine learning capabilities: The induction of scheduling knowledge, IIE Transaction 24(1992)156-168.
20. P.H. Winston, Learning structural descriptions from examples, in: The Psychology of Computer Vision, P.H. Winston, ed., McGraw-Hill, New York, 1975.
21. Y. Yoon, T. Fuimaraes and G. Swales, Integrating artificial networks with rule-based expert systems, Decision Support Systems 11(1994)497-507.
22. R-T. Zhang and G. Veenker, Neural networks that teach themselves through genetic discovery of novel examples, in: 1991 IEEE International Joint Conference on Neural Networks, Vol. 1, IEEE, New York, 1991, pp. 690-695.