Semi-supervised Learning for Regression with Co-training by Committee

Authors:
Mohamed Farouk Abdel Hady;Friedhelm Schwenker;Günther Palm
Affiliations:
Institute of Neural Information Processing, University of Ulm, Ulm, Germany D-89069;Institute of Neural Information Processing, University of Ulm, Ulm, Germany D-89069;Institute of Neural Information Processing, University of Ulm, Ulm, Germany D-89069
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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Abstract

Semi-supervised learning is a paradigm that exploits the unlabeled data in addition to the labeled data to improve the generalization error of a supervised learning algorithm. Although in real-world applications regression is as important as classification, most of the research in semi-supervised learning concentrates on classification. In particular, although Co-Training is a popular semi-supervised learning algorithm, there is not much work to develop new Co-Training style algorithms for semi-supervised regression. In this paper, a semi-supervised regression framework, denoted by CoBCReg is proposed, in which an ensemble of diverse regressors is used for semi-supervised learning that requires neither redundant independent views nor different base learning algorithms. Experimental results show that CoBCReg can effectively exploit unlabeled data to improve the regression estimates.