Advertisement

The Use of Compound Attributes inAQ Learning

  • Janusz Wojtusiak
  • Ryszard S. Michalski
Part of the Advances in Soft Computing book series (AINSC, volume 35)

Abstract

Compound attributes are named groups of attributes that have been introduced in Attributional Calculus (AC) to facilitate learning descriptions of objects whose components are characterized by different subsets of attributes. The need for such descriptions appears in many practical applications. A method for handling compound attributes in AQ learning and testing is described and illustrated by examples.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    1. Dey, D. and Sarkar, S. (1996) A Probabilistic Relational Model and Algebra. ACM Transactions on Database Systems (TODS), 21, Issue 3Google Scholar
  2. 2.
    2. Dietterich, T. G. and Michalski, R. S. (1981) Inductive Learning of Structural Descriptions: Evaluation Criteria and Comparative Review of Selected Methods. Artificial Intelligence Journal, Vol. 16, No. 3, 257–294CrossRefMathSciNetGoogle Scholar
  3. 3.
    3. Getoor, L., Friedman, N., Koller, D., Taskar, B. (2001) Probabilistic Models of Relational Structure. International Conference on Machine Learning, ICML'01, Williamstown, MAGoogle Scholar
  4. 4.
    4. Kaufman, K. and Michalski, R. S. (2000) An Adjustable Rule Learner for Pattern Discovery Using the AQ Methodology. Journal of Intelligent Information Systems, 14, 199–216CrossRefGoogle Scholar
  5. 5.
    5. Larson, J. and Michalski, R. S. (1977) Inductive Inference of VL Decision Rules,” Invited paper for the Workshop in Pattern-Directed Inference Systems, Hawaii, and published in SIGART Newsletter, ACM, No. 63, 38-44.Google Scholar
  6. 6.
    6. Lavrac, N. and Dzeroski, S. (1994) Inductive Logic Programming: Techniques and Applications, Ellis HorwoodGoogle Scholar
  7. 7.
    7. Michalski, R. S. (1980) Pattern Recognition as Rule-Guided Inductive Inference. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 4, 349–361CrossRefGoogle Scholar
  8. 8.
    8. Michalski, R. S. (2004) ATTRIBUTIONAL CALCULUS: A Logic and Representation Language for Natural Induction. Reports of the Machine Learning and Inference Laboratory, MLI 04-2, George Mason University, Fairfax, VAGoogle Scholar
  9. 9.
    9. Michalski, R. S. (2004) Generating Alternative Hypotheses in AQ Learning. Reports of the Machine Learning and Inference Laboratory, MLI 04-6, George Mason University, Fairfax, VAGoogle Scholar
  10. 10.
    10. Michalski, R. S. and Kaufman, K. (2001) The AQ19 System for Machine Learning and Pattern Discovery: A General Description and User's Guide. Reports of the Machine Learning and Inference Laboratory, MLI 01-2, George Mason University, Fairfax, VAGoogle Scholar
  11. 11.
    11. Michalski, R. S. and Wojtusiak, J. (2005) Reasoning with Meta-values in AQ Learning. Reports of the Machine Learning and Inference Laboratory, MLI 05-1, George Mason University, Fairfax, VAGoogle Scholar
  12. 12.
    12. Michalski, R.S. and Wojtusiak, J. (2006) Semantic and Syntactic Attribute Types in AQ Learning. Reports of the Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA (to appear)Google Scholar
  13. 13.
    13. Wojtusiak, J. (2004) AQ21 User's Guide. Reports of the Machine Learning and Inference Laboratory, MLI 04-3, George Mason University, Fairfax, VAGoogle Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Janusz Wojtusiak
    • 1
  • Ryszard S. Michalski
    • 1
    • 2
  1. 1.Machine Learning and Inference Laboratory George Mason UniversityFairfaxUSA
  2. 2.Institute of Computer Science Polish Academy of SciencesWarsawPoland

Personalised recommendations