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Research on Query-by-Committee Method of Active Learning and Application

  • Yue Zhao
  • Ciwen Xu
  • Yongcun Cao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, then selecting the most informative ones with respect to a given cost function for a human to label. The major problem is to find the best selection strategy function to quickly reach high classification accuracy. Query-by-Committee (QBC) method of active learning is less computation than other active learning approaches, but its classification accuracy can not achieve the same high as passive learning. In this paper, a new selection strategy for the QBC method is presented by combining Vote Entropy with Kullback-Leibler divergence. Experimental results show that the proposed algorithm is better than previous QBC approach in classification accuracy. It can reach the same accuracy as passive learning with few labeled training examples.

Keywords

Classification Accuracy Committee Member Unlabeled Data Passive Learning Active Learning Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yue Zhao
    • 1
  • Ciwen Xu
    • 1
  • Yongcun Cao
    • 1
  1. 1.School of Mathematics and Computer ScienceCentral University for NationalitiesBeijingChina

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