A Neural Network Approach to Constructive Induction

Proceedings of the 8th International Workshop on Machine Learning, ICML 1991

Volumn , Issue , 1991, Pages 228-232

  Yeung, Dit Yan ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ARTS COMPUTING; MULTILAYER NEURAL NETWORKS;

AUTOMATIC-LEARNING; CONSTRUCTIVE INDUCTION; DYNAMIC NETWORK; HUMAN INTERVENTION; INDUCTIVE LEARNING; LEARNING PROBLEM; LEARNING PROCESS; MULTILAYER FEEDFORWARD NETWORKS; NEURAL-NETWORKS; SYSTEMATIC SEARCHES;

LEARNING SYSTEMS;

EID: 33747395700     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/B978-1-55860-200-7.50049-0     Document Type: Conference Paper

References (7)

1. T. Ash (1989). Dynamic node creation in backpropagation networks. Technical Report ICS 8901, Institute for Cognitive Science, University of California at San Diego.
2. S. E. Fahlman (1988). An empirical study of learning speed in backpropagation networks. Technical Report CMU-CS-88-162, School of Computer Science, Carnegie Mellon University.
3. S. E. Fahlman & C. Lebiere (1990). The cascade-correlation learning architecture. Technical Report CMU-CS-90-100, School of Computer Science, Carnegie Mellon University.
4. K. Hornik, M. Stinchcombe & H. White (1989). Multilayer feedforward networks are universal approximators. Neural Networks, vol. 2, pp. 359-366.
5. F. Rosenblatt (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. New York: Spartan.
6. D. E. Rumelhart, G. E. Hinton & R. J. Williams (1986). Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1, D. E. Rumelhart and J. L. McClelland (eds.), Cambridge, Massachusetts: MIT Press, pp. 318-364.
7. B. Widrow & M. E. Hoff (1960). Adaptive switching circuits. In IRE WESCON Convention Record, part 4, pp. 96-104.