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Conference Paper

Observational Learning with Modular Networks

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84217

Shin,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Shin, H., Lee, H., & Cho, S. (2000). Observational Learning with Modular Networks. In International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) (pp. 126-132). Heidelberg: Springer-Verlag.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E4A8-B
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.