Abstract
Observational learning algorithm is an ensemble algorithm where each network is initially trained with a bootstrapped data set and virtual data are generated from the ensemble for training. Here we propose a modular OLA approach where the original training set is partitioned into clusters and then each network is instead trained with one of the clusters. Networks are combined with different weighting factors now that are inversely proportional to the distance from the input vector to the cluster centers. Comparison with bagging and boosting shows that the proposed approach reduces generalization error with a smaller number of networks employed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cho, S. and Cha, K., “Evolution of neural network training set through addition of virtual samples,” International Conference on Evolutionary Computations, 685–688 (1996)
Cho, S., Jang, M. and Chang, S., “Virtual Sample Generation using a Population of Networks,” Neural Processing Letters, Vol. 5 No. 2, 83–89 (1997)
Jang, M. and Cho, S., “Observational Learning Algorithm for an Ensemble of Neural Networks,” submitted (1999)
Drucker, H., “Improving Regressors using Boosting Techniques,” Machine Learning: Proceedings of the Fourteenth International Conference, 107–115 (1997)
Perrone, M. P. and Cooper, L. N., “When networks disagree: Ensemble methods for hybrid neural networks,” Artificial Neural Networks for Speech and Vision, (1993)
Platt, J., “A Resource-Allocating Network for Function Interpolation,” Neural Computation, Vol 3, 213–225 (1991)
Roberts, S. and Tarassenko, L., “A Probabilistic Resource Allocating Network for Novelty Detection,” Neural Computation, Vol 6, 270–284 (1994)
Sebestyen, G. S., “Pattern Recognition by an Adaptive Process of Sample Set Construction,” IRE Trans. Info. Theory IT-8, 82–91 (1962)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shin, H., Lee, H., Cho, S. (2000). Observational Learning with Modular Networks. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_19
Download citation
DOI: https://doi.org/10.1007/3-540-44491-2_19
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41450-6
Online ISBN: 978-3-540-44491-6
eBook Packages: Springer Book Archive