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Using co-training and self-training in semi-supervised multiple classifier systems

Published: 17 August 2006 Publication History

Abstract

Multiple classifier systems have been originally proposed for supervised classification tasks, and few works have dealt with semi-supervised multiple classifiers. However, there are important pattern recognition applications, such as multi-sensor remote sensing and multi-modal biometrics, which demand semi-supervised multiple classifier systems able to exploit both labelled and unlabelled data. In this paper, the use, in multiple classifier systems, of two well known semi-supervised learning methods, namely, co-training and self-training, is investigated by experiments. Reported results on benchmarking data sets show that co-training and self-training allow exploiting unlabelled data in different types of multiple classifiers systems.

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  • (2012)Bagging, bumping, multiview, and active learning for record linkage with empirical results on patient identity dataComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2012.08.003108:3(1160-1169)Online publication date: 1-Dec-2012
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  1. Using co-training and self-training in semi-supervised multiple classifier systems

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      cover image Guide Proceedings
      SSPR'06/SPR'06: Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
      August 2006
      936 pages
      ISBN:3540372369
      • Editors:
      • Dit-Yan Yeung,
      • James T. Kwok,
      • Ana Fred,
      • Fabio Roli,
      • Dick de Ridder

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      Springer-Verlag

      Berlin, Heidelberg

      Publication History

      Published: 17 August 2006

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      • (2015)Partially-supervised learning from facial trajectories for face recognition in video surveillanceInformation Fusion10.1016/j.inffus.2014.05.00624:C(31-53)Online publication date: 1-Jul-2015
      • (2015)An adaptive ensemble-based system for face recognition in person re-identificationMachine Vision and Applications10.1007/s00138-015-0697-726:6(741-773)Online publication date: 1-Aug-2015
      • (2012)Bagging, bumping, multiview, and active learning for record linkage with empirical results on patient identity dataComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2012.08.003108:3(1160-1169)Online publication date: 1-Dec-2012
      • (2012)Clustering-based feature selection for content based remote sensing image retrievalProceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I10.1007/978-3-642-31295-3_50(427-435)Online publication date: 25-Jun-2012
      • (2010)Semi-supervised dependency parsing using generalized tri-trainingProceedings of the 23rd International Conference on Computational Linguistics10.5555/1873781.1873901(1065-1073)Online publication date: 23-Aug-2010
      • (2010)Co-training with relevant random subspacesNeurocomputing10.1016/j.neucom.2010.01.01873:10-12(1652-1661)Online publication date: 1-Jun-2010
      • (2009)Random relevant and non-redundant feature subspaces for co-trainingProceedings of the 10th international conference on Intelligent data engineering and automated learning10.5555/1789574.1789665(679-686)Online publication date: 23-Sep-2009
      • (2009)The Impact of Reliability Evaluation on a Semi-supervised Learning ApproachProceedings of the 15th International Conference on Image Analysis and Processing10.1007/978-3-642-04146-4_28(249-258)Online publication date: 29-Aug-2009
      • (2007)A co-training approach for time series prediction with missing dataProceedings of the 7th international conference on Multiple classifier systems10.5555/1761171.1761183(93-102)Online publication date: 23-May-2007

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