skip to main content
10.1145/279943.279962acmconferencesArticle/Chapter ViewAbstractPublication PagescoltConference Proceedingsconference-collections
Article
Free Access

Combining labeled and unlabeled data with co-training

Authors Info & Claims
Published:24 July 1998Publication History
First page image

References

  1. 1.M. Craven, D. Freitag, A. McCallum, T. Mitchell, K. Nigam, artd C.Y. Quek. Learning to extract symbolic knowledge from the world wide web. Technical report, Carnegie Mellon University, January 1997.Google ScholarGoogle Scholar
  2. 2.S. E. Decatur. PAC learning with constantpartition classification noise and applications to decision tree induction. In Proceedings of the Fourteenth International Conference on Machine Learnrag, pages 83 -91, July 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. 3.A.P. Dempster, N.M. Laird, and D.B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B, 39:1 38, 1!)77.Google ScholarGoogle Scholar
  4. 4.Richard O. Duda and Peter E. Hart. Pattern Classification and Scene Analysis. Wiley, 1973.Google ScholarGoogle Scholar
  5. 5.Z. Ghahramani and M. I. Jordan. Supervised learning from incomplete data via an EM approach. In Advances in Neural Information Processing Systems (NIPS 6). Morgan Kauffman, 1994.Google ScholarGoogle Scholar
  6. 6.S. A. Goldman and M. J. Kearns. On the complexi ty of teaching. Journal of Computer and System Sciences, 50({):20-31, February 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. 7.A.G. Hauptmann and M.J. Witbrock. Informedia: News-on-demand - multimedia information acquisition and retrieval. In M. Maybury, editor, Intelligent Multimedia Information Retrieval, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. 8.J. Jackson and A. Tomkins. A computational model of teaching. In Proceedings of the Fifth Annual Workshop on Uomputational Learning Theo ry, pages 319 326. Morgan Kaufmann, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. 9.D. R. Karger. Random sampling in cut, flow, and network design problems. In Proceedings of the Twent:q-Si:rth Anrrual ACM Symposium on the Theory of Computing, pages 648-657, May 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. 10.D. R. Karger. Randonl sampling in cut, flow, and network design problems. Journal version draft, 1997.Google ScholarGoogle Scholar
  11. 11.M. Kearns. Efficient noise-tolerant learning from statistical queries. In Proceedings of the Twenty- Fifth Annual A CM ,qym. posium on Theory of Computing, pages 392-40l. 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. 12.D. D. Lewis and M. Ringuette. A comparison of two learning algorithms for text categorization. In Third Annual Symposium on Document Analysis and Information Retrieval, pages 81-93, 1994.Google ScholarGoogle Scholar
  13. 13.Joel Ratsaby and Santosh S. Venkatesh. Learning from a mixture of labeled and unlabeled examples with parametric side information. In Proceedings of the 8th Annual Conference on Computational Learning Theory, pages 412-417. ACM Press, New York, NY, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. 14.M.J. Witbrock and A.G. Hauptmann. Improving acoustic models by watching television. Technical Report CMU-CS-98-110, Carnegie Mellon University, March 19 1998.Google ScholarGoogle ScholarCross RefCross Ref
  15. 15.D. Yarowsky. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedlags of the SSrd Annual Meeting of the Association for Computational Linguistics, pages 189-196, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Combining labeled and unlabeled data with co-training

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          COLT' 98: Proceedings of the eleventh annual conference on Computational learning theory
          July 1998
          304 pages
          ISBN:1581130570
          DOI:10.1145/279943

          Copyright © 1998 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 24 July 1998

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • Article

          Acceptance Rates

          Overall Acceptance Rate35of71submissions,49%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader