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Observational Learning with Modular Networks

2000

Conference Paper

ei


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.

Author(s): Shin, H. and Lee, H. and Cho, S.
Journal: Lecture Notes in Computer Science (LNCS 1983)
Volume: LNCS 1983
Pages: 126-132
Year: 2000
Month: July
Day: 0
Publisher: Springer-Verlag

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
Event Place: HK, China

Address: Heidelberg
Digital: 0
Institution: Seoul National University, Seoul, Korea
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{2691,
  title = {Observational Learning with Modular Networks},
  author = {Shin, H. and Lee, H. and Cho, S.},
  journal = {Lecture Notes in Computer Science (LNCS 1983)},
  volume = {LNCS 1983},
  pages = {126-132},
  publisher = {Springer-Verlag},
  organization = {Max-Planck-Gesellschaft},
  institution = {Seoul National University, Seoul, Korea},
  school = {Biologische Kybernetik},
  address = {Heidelberg},
  month = jul,
  year = {2000},
  month_numeric = {7}
}