Evaluation of an anti-regularization technique in neural networks

Proceedings - International Conference on Pattern Recognition

Volumn 4, Issue , 1996, Pages 205-208

  Hamamoto, Y ^a   Mitani, Y ^a   Ishihara, H ^a   Hase, T ^a   Tomita, S ^a  

^a YAMAGUCHI UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

SOFTWARE ENGINEERING;

GENERALIZATION ABILITY; LEARNING TIME; SMALL TRAINING;

PATTERN RECOGNITION;

EID: 84880660377     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.1996.547416     Document Type: Conference Paper

References (6)

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3. F. Girosi, M. Jones and T. Poggio. Regularization theory and neural networks architectures. Neural Computation, 7 :219-269, 1995.
4. R. Reed. Pruning algorithms -A survey. IEEE Trans. Neural Networks, 4(5) :740-747, 1993.
5. Y. Chauvin. Generalization performance of overtrained back-propagation networks. in Neural Networks, Proc. EUROSIP Workshop, L. B. Almeida and C. J. Wellekens, Eds., :46-55, 1990.
6. A. K. Jain and B. Chandrasekaran. Dimensionality and sample size considerations in pattern recognition practice. in Handbook of Statistics, 2, P. R. Krishnaiah and L. N. Kanal, Eds., North-Holland Publishing Company, :835-855, 1982.