Abstract
Constructive algorithms can be classified in two main groups: freezing and non-freezing, each one having its own advantages and inconveniences. In large scale problems, freezing algorithms are more suitable thanks to their speed. The main problem of these algorithms, however, comes from the fixed-size nature of the new units that they use. In this paper, we present a new freezing algorithm which constructs the main network by adding small and variable-size accessory networks trained by a non-freezing algorithm instead of simple units...
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
T.Y. Kwok and D.Y. Yeung, Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems. IEEE Trans. on Neural Networks, vol. 8, no. 3, pp. 630–645, May 1997.
S.E. Fahlman and C. Lebiere, The Cascade-Correlation Learning Architecture, in Advances in Neural Information Processing Systems 2, D.S. Touretzky Ed., pp. 524–532, Morgan Kaufmann, Los Altos CA, 1990.
J.H. Friedman and W. Stuetzle, Projection Pursuit Regression, Journal of American Statistical Association, vol. 76, no. 376, pp. 817–823, 1981.
T. Ash, Dynamic Node Creation in Backpropagation networks, Connection Sciences, vol. 1, no. 4, pp. 365–375, 1989.
Ch. Jutten and R. Chentouf, A New Scheme for Incremental Learning, Neural Processing Letters, vol. 2, no. 1, pp. 1–4, 1995.
T.Y. Kwok and D.Y. Yeung, Experimental Analysis of Input Weight Freezing in Constructive Neural Networks, In Proceedings of the IEEE International Conference on Neural Networks, vol. 1, pp. 511–516, San Francisco, California, USA, 1993.
T.Y. Kwok and D.Y. Yeung, Objective Functions for Training New Hidden Units in Constructive Neural Networks, IEEE Transaction on Neural Networks, vol. 8, no. 5, pp. 1131–1148, September 1997.
N.A.C. Cressie, Statistics for Spatial Data, John Wiley & sons, New York, 1991.
Sh. Hosseini and Ch. Jutten, Simultaneous Estimation of Signal and Noise by Constructive Neural Networks, In Proceedings of International ICSC/IFAC Symposium on Neural Computation, Vienna, Austria, September 1998.
G. Baillargeon, Méthodes Statistiques de l'Ingénieur. volume 1. Les éditions SMG, 1994.
Y. Le Cun, J.S. Denker, and S. A. Solla, Optimal Brain Damage, in Advanced in Neural Information Processing (2), D.S. Touretzky Ed., pp. 598–605, Morgan Kaufmann, 1990.
M. Lehtocangas, J. Saarinen and K. Kaski, Fine-tuning cascade-correlation feedforward network trained with backpropagation, Neural Processing Letters, vol. 2, no. 2, pp. 10–12, 1995.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hosseini, S., Jutten, C. (1999). Weight freezing in constructive neural networks: A novel approach. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100467
Download citation
DOI: https://doi.org/10.1007/BFb0100467
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66068-2
Online ISBN: 978-3-540-48772-2
eBook Packages: Springer Book Archive