Learning with hybrid data

This source preferred by Hamid Bouchachia

This data was imported from DBLP:

Authors: Bouchachia, A.

Editors: Nedjah, N., Mourelle, L.D.M., Abraham, A. and Köppen, M.


Journal: HIS

Pages: 193-200

Publisher: IEEE Computer Society

DOI: 10.1109/ICHIS.2005.68

This data was imported from Scopus:

Authors: Bouchachia, A.

Journal: Proceedings - HIS 2005: Fifth International Conference on Hybrid Intelligent Systems

Volume: 2005

Pages: 193-198

DOI: 10.1109/ICHIS.2005.68

Learning with hybrid data aims at inducing a classifier that learns from partly labeled data. In this paper, four semi-supervised learning (SSL) methods are discussed. These include clustering with partial supervision, active sampling for learning with RBF networks, Gaussian mixture models based on the EM method, and finally seed-based clustering. The empirical study shows that the effect of unlabeled data on the accuracy for some algorithms is significant, while that of others depends on the data and the assumptions underlying the algorithms themselves. © 2005 IEEE.

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