Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: Inductive Learning

Inductive learning methods can be defined as those methods that systematically produce general descriptions or knowledge from the specific knowledge provided by domain examples.

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  1. Learning to Extract Relations from the Web using Minimal Supervision
    [Details] [PDF]
    Razvan C. Bunescu and Raymond J. Mooney
    In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL'07), Prague, Czech Republic, June 2007.
  2. Multiple Instance Learning for Sparse Positive Bags
    [Details] [PDF]
    Razvan C. Bunescu and Raymond J. Mooney
    In Proceedings of the 24th Annual International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.
  3. Creating Diverse Ensemble Classifiers to Reduce Supervision
    [Details] [PDF]
    Prem Melville
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, November 2005. 141 pages. Technical Report TR-05-49.
  4. Active Feature-Value Acquisition for Classifier Induction
    [Details] [PDF]
    Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney
    Technical Report UT-AI-TR-04-311, Artificial Intelligence Lab, University of Texas at Austin, February 2004.
  5. Active Feature-Value Acquisition for Classifier Induction
    [Details] [PDF]
    Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney
    In Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM-2004), 483-486, Brighton, UK, November 2004.
  6. Diverse Ensembles for Active Learning
    [Details] [PDF]
    Prem Melville and Raymond J. Mooney
    In Proceedings of 21st International Conference on Machine Learning (ICML-2004), 584-591, Banff, Canada, July 2004.
  7. Experiments on Ensembles with Missing and Noisy Data
    [Details] [PDF]
    Prem Melville, Nishit Shah, Lilyana Mihalkova, and Raymond J. Mooney
    In F. Roli, J. Kittler, and T. Windeatt, editors, {Lecture Notes in Computer Science:} Proceedings of the Fifth International Workshop on Multi Classifier Systems (MCS-2004), 293-302, Cagliari, Italy, June 2004. Springer Verlag.
  8. Creating Diversity in Ensembles Using Artificial Data
    [Details] [PDF]
    Prem Melville and Raymond J. Mooney
    Journal of Information Fusion: Special Issue on Diversity in Multi Classifier Systems, 6(1):99-111, 2004.
  9. Creating Diverse Ensemble Classifiers
    [Details] [PDF]
    Prem Melville
    Technical Report UT-AI-TR-03-306, Department of Computer Sciences, University of Texas at Austin, December 2003. Ph.D. proposal.
  10. Constructing Diverse Classifier Ensembles Using Artificial Training Examples
    [Details] [PDF]
    Prem Melville and Raymond J. Mooney
    In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-2003), 505-510, Acapulco, Mexico, August 2003.
  11. Property-Based Feature Engineering and Selection
    [Details] [PDF]
    Noppadon Kamolvilassatian
    Masters Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, December 2002. 85 pages.
  12. Encouraging Experimental Results on Learning CNF
    [Details] [PDF]
    Raymond J. Mooney
    Machine Learning, 19(1):79-92, 1995.
  13. Growing Layers of Perceptrons: Introducing the Extentron Algorithm
    [Details] [PDF]
    Paul T. Baffes and John M. Zelle
    In Proceedings of the 1992 International Joint Conference on Neural Networks, 392--397, Baltimore, MD, June 1992.
  14. Using Explanation-Based and Empirical Methods in Theory Revision
    [Details] [PDF]
    Dirk Ourston
    PhD Thesis, Department of Computer Science, University of Texas at Austin, 1991.
  15. Symbolic and Neural Learning Algorithms: An Experimental Comparison
    [Details] [PDF]
    J.W. Shavlik, Raymond J. Mooney and G. Towell
    Machine Learning, 6:111-143, 1991. Reprinted in {it Readings in Knowledge Acquisition and Learning}, Bruce G. Buchanan and David C. Wilkins (eds.), Morgan Kaufman, San Mateo, CA, 1993..
  16. An Experimental Comparison of Symbolic and Connectionist Learning Algorithms
    [Details] [PDF]
    Raymond J. Mooney, J.W. Shavlik, G. Towell and A. Gove
    In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), 775-780, Detroit, MI, August 1989. Reprinted in ``Readings in Machine Learning'', Jude W. Shavlik and T. G. Dietterich (eds.), Morgan Kaufman, San Mateo, CA, 1990..