Learning with Hybrid Data

Authors:
Abdelhamid Bouchachia
Affiliations:
University of Klagenfurt, Austria
Venue:
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Year:
2005

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Abstract

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 seedbased 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.