Advertisement

A Collaborative Ability Measurement for Co-training

  • Dan Shen
  • Jie Zhang
  • Jian Su
  • Guodong Zhou
  • Chew-Lim Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3248)

Abstract

This paper explores collaborative ability of co-training algorithm. We propose a new measurement (CA) for representing the collaborative ability of co-training classifiers based on the overlapping proportion between certain and uncertain instances. The CA measurement indicates whether two classifiers can co-train effectively. We make theoretical analysis for CA values in co-training with independent feature split, with random feature split and without feature split. The experiments justify our analysis. We also explore two variations of the general co-training algorithm and analyze them using the CA measurement.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Collins, M., Singer, Y.: Unsupervised models for named entity classification. In: Proceedings of the 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Proceeding and Very Large Corpora (1999)Google Scholar
  2. 2.
    Muslea, S.M., Knoblock, C.A.: Selective sampling with redundant views. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence (2000)Google Scholar
  3. 3.
    Ng, V., Cardie, C.: Weakly supervised natural language learning without redundant views. In: Proceedings of the Main Conference on HLT-NAACL 2003 (2002)Google Scholar
  4. 4.
    Blum, A., Mitchell, T.: Combining labeled data and unlabelled data with co-training. In: Proceedings of the 11th Annual Conference on Computational learning Theory (1998)Google Scholar
  5. 5.
    Y. B., Cao, H.L., Lian, L.: Uncertainty reduction in collaborative bootstrapping: Measure and algorithm. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (2003)Google Scholar
  6. 6.
    Nigam, K., Ghani, R.: Analyzing the effectiveness and applicability of co-training. In: Proceedings of the 9th International Conference on Information and Knowledge Management (2000)Google Scholar
  7. 7.
    Joachims, T.: Making large-scale svm learning practical. In: Scholkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, B, MIT Press, Cambridge (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dan Shen
    • 1
    • 2
  • Jie Zhang
    • 1
    • 2
  • Jian Su
    • 1
  • Guodong Zhou
    • 1
  • Chew-Lim Tan
    • 2
  1. 1.Institute for Infocomm ResearchSingapore
  2. 2.Department of Computer ScienceNational University of SingaporeSingapore

Personalised recommendations