Combining Committee-Based Semi-supervised and Active Learning and Its Application to Handwritten Digits Recognition

  • Mohamed Farouk Abdel Hady
  • Friedhelm Schwenker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5997)


Semi-supervised learning reduces the cost of labeling the training data of a supervised learning algorithm through using unlabeled data together with labeled data to improve the performance. Co-Training is a popular semi-supervised learning algorithm, that requires multiple redundant and independent sets of features (views). In many real-world application domains, this requirement can not be satisfied. In this paper, a single-view variant of Co-Training, CoBC (Co-Training by Committee), is proposed, which requires an ensemble of diverse classifiers instead of the redundant and independent views. Then we introduce two new learning algorithms, QBC-then-CoBC and QBC-with-CoBC, which combines the merits of committee-based semi-supervised learning and committee-based active learning. An empirical study on handwritten digit recognition is conducted where the random subspace method (RSM) is used to create ensembles of diverse C4.5 decision trees. Experiments show that these two combinations outperform the other non committee-based ones.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mohamed Farouk Abdel Hady
    • 1
  • Friedhelm Schwenker
    • 1
  1. 1.Institute of Neural Information ProcessingUniversity of UlmUlmGermany

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