Some Machine Learning Papers

These papers may be obtained from the archive of the Machine Learning Group headed by Professor Ray Mooney at the University of Texas
  1. An Inductive Logic Programming Method for Corpus-based Parser Construction [Abstract] [Gzipped PS] [PDF]
    John M. Zelle and Raymond J. Mooney
    Unpublished Technical Note, January 1997

  2. Learning to Parse Database Queries using Inductive Logic Programming [Abstract] [Gzipped PS] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the Thirteenth National Conference on Aritificial Intelligence, pp. 1050-1055, Portland, OR, August, 1996. (AAAI-96)

  3. Comparative Results on Using Inductive Logic Programming for Corpus-based Parser Construction [Abstract] [Gzipped PS] [PDF]
    John M. Zelle and Raymond J. Mooney
    Symbolic, Connectionist, and Statistical Approaches to Learning for Natural Language Processing, S. Wermter, E. Riloff and G. Scheler, Eds, Spring Verlag, 1996.

  4. Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers [Abstract] [Gzipped PS] [PDF]
    John M. Zelle
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, August, 1995. (Technical Report AI96-249)

  5. A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Constuction [Abstract] [Gzipped PS] [PDF]
    John M. Zelle and Raymond J. Mooney
    Working Notes of the IJCAI-95 Workshop on New Approaches to Learning for Natural Language Processing, pp.79-86, Montreal, Quebec, August, 1995.

  6. Inducing Logic Programs without Explicit Negative Examples [Abstract] [Gzipped PS] [PDF]
    John M. Zelle, Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney
    Proceedings of the Fifth International Workshop on Inductive Logic Programming, Leuven, Belguim, Sepetember 1995.

  7. Combining Top-Down And Bottom-Up Techniques In Inductive Logic Programming [Abstract] [Gzipped PS] [PDF]
    John M. Zelle, Raymond J. Mooney and Joshua B. Konvisser
    Proceedings of the Eleventh International Workshop on Machine Learning, pp. 343-351, Rutgers, NJ, July 1994. (ML-94)

  8. Inducing Deterministic Prolog Parsers From Treebanks: A Machine Learning Approach [Abstract] [Gzipped PS] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the Twelfth National Conference on AI, pp. 748-753, Seattle, WA, July 1994. (AAAI-94)

  9. Integrating ILP and EBL [Abstract] [Gzipped PS] [PDF]
    Raymond J. Mooney and John M. Zelle
    SIGART Bulletin, Volume 5, number 1, Jan. 1994, pp 12-21.

  10. Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition [Abstract] [Gzipped PS] [PDF]
    John M. Zelle
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, 1993.

  11. Combining FOIL and EBG to Speed-Up Logic Programs [Abstract] [Gzipped PS] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1106-1111, Chambery, France, 1993. (IJCAI-93)

  12. Learning Semantic Grammars With Constructive Inductive Logic Programming [Abstract] [Gzipped PS] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the Eleventh National Conference of the American Association for Artificial Intelligence, pp. 817-822, Washington, D.C. July 1993 (AAAI-93).

  13. Speeding-up Logic Programs by Combining EBG and FOIL [Abstract] [Gzipped PS] [PDF]
    John M. Zelle and Raymond J. Mooney
    Proceedings of the 1992 Machine Learning Workshop on Knowledge Compilation and Speedup Learning, Aberdeen Scotland, July 1992.

  14. Growing Layers of Perceptrons: Introducing the Extentron Algorithm [Abstract] [Gzipped PS] [PDF]
    Paul T. Baffes and John M. Zelle
    Proceedings of the 1992 International Joint Conference on Neural Networks, pp. 392-397, Baltimore, Maryland, June 1992.