Learning from examples with unspecified attribute values

Information and Computation

Volumn 180, Issue 2, 2003, Pages 82-100

  Goldman, Sally A ^a   Kwek, Stephen S ^b   Scott, Stephen D ^c  

^a WASHINGTON UNIVERSITY   (United States)

^b University of Texas at San Antonio   (United States)

^c UNIVERSITY OF NEBRASKA LINCOLN   (United States)

Author keywords

Decision trees; DNF formulas; Equivalence queries; Exact learning; Membership queries; Unspecified or missing attributes

Indexed keywords

ALGORITHMS; INFORMATION ANALYSIS; POLYNOMIALS; TREES (MATHEMATICS);

LEARNING MODELS;

LEARNING SYSTEMS;

EID: 0037471717     PISSN: 08905401     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0890-5401(02)00030-5     Document Type: Article

References (32)

1. D. Angluin, Queries and concept learning, Machine Learning 2 (4) (1988) 319-342.
2. D. Angluin, L. Hellerstein, M. Karpinski, Learning read-once formulas with queries, Journal of ACM 40 (1993) 185-210.
3. R. Bareiss, B.W. Porter, R. Holte, Concept learning and heuristic classification in weak-theory domains, Artificial Intelligence 45 (1990) 229-263.
4. S. Ben-David, E. Dichterman, Learning with restricted focus of attention, Journal of Computer and System Sciences 56 (3) (1998) 277-298.
5. A. Birkendorf, E. Dichterman, J. Jackson, N. Klasner, H.U. Simon, On restricted-focus-of-attention learnability of Boolean functions, Machine Learning 30 (1998) 89-123.
6. A. Birkendorf, N. Klasner, C. Kuhlmann, H.U. Simon, Structural results about exact learning with unspecified attribute values, Journal of Computer and System Sciences 60 (2) (2000) 258-277.
7. M. Blum, A. Chandra, M. Wegman, Equivalence of free Boolean graphs can be decided probabilistically in polynomial time, Information Processing Letters 10 (1980) 80-82.
8. L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees, Wadsworth International Group, 1984.
9. R.E. Bryant, Symbolic boolean manipulation with ordered binary decision diagrams, ACM Computing Surveys 24 (1992)293-318.
10. N. Bshouty, Exact learning via the monotone theory, Information and Computation 123 (1) (1995) 146-153.
11. N.H. Bshouty, R. Cleve, S. Kannan, C. Tamon, Oracles and queries that are sufficient for exact learning, J. of Comput. Syst. Sci. 52 (3) (1996) 421-433.
12. N. Bshouty, D. Wilson, On learning in the presence of unspecified attribute values, in: Proceedings of the 12th Annual Conference on Computation and Learning Theory, ACM Press, New York, NY, 1999, pp. 81-87.
13. R. Caruana, Multitask learning, Machine Learning 28 (1997) 41-75.
14. R. Caruana, S. Baluja, T. Mitchell, Using the future to "sort out" the present: Rankprop and multitask learning for medical risk evaluation, Advances in Neural Information Processing Systems 8 (1996) 959-965.
15. M, Fine, D. Singer, B. Hanusa, J. Lave, W. Kapoor, Validation of a pneumonia prognostic index using the MedisGroups comparative hospital database, American Journal of Medicine 94 (1993).
16. S. Fortune, J. Hopcroft. E. Schmidt, The complexity of equivalence and containment for free single variable program schemes, in: Lecture Notes in Computer Science, vol. 62, Proceedings of Automata, Languages and Programming, Springer, 1978, pp. 227-240.
17. R. Gavalda, D. Guijarro, Learning ordered binary decision diagrams, in: Proceedings of the 6th International Workshop on Algorithmic Learning Theory, Springer, 1995, pp. 228-238.
18. S.A. Goldman, S.S. Kwek, S.D. Scott, Learning from examples with unspecified attribute values, in: Proceedings of the 10th Annual Conference on Computational Learning Theory, ACM Press, New York. NY, 1997, pp. 231-242.
19. R. Greiner, A.J. Grove, D. Roth, Learning active classifiers, in: Proceedings of the 13th International Conference on Machine Learning, Morgan Kaufmann, 1996, pp. 207-215.
20. T. Hancock, Identifying μ-formula decision trees with queries, in: Proceedings of the Third Annual Workshop on Computational Learning Theory, Morgan Kaufmann, 1990, pp. 23-37.
21. L. Hellerstein, M. Karpinski, Learning read-once formulas using membership queries, in: Proceedings of the Second Annual Workshop on Computational Learning Theory, Morgan Kaufmann, 1989, pp. 146-161.
22. H. Hunt, R. Stearns, Monotone Boolean formulas, distributive lattices, and the complexity of logics, algebraic structures, and computation structures, in: Lecture Notes in Computer Science. Proceedings of 3rd Annual Symposium on Theoretical Aspects of Computer Science, Springer, 1986, pp. 277-287.
23. N.D. Jones, W.T. Laaser, Complete problems for deterministic polynomial time, in: Theoretical Computer Science, vol. 3, 1977, pp. 107-113.
24. M. Kearns, R. Schapire, Efficient distribution-free learning of probabilistic concepts, Journal of Computer and System Sciences 48 (3) (1994) 464-497.
25. R. Khardon, D. Roth, Learning to reason with a restricted view, Machine Learning 35 (2) (1999) 95-117.
26. K. Pillaipakkamnatt, V. Raghavan, Read-twice DNF formulas are properly learnable, Information and Computation 122 (2) (1995) 236-267.
27. J.R. Quinlan, Unknown attribute values in induction, in: Proceedings of the 6th International Machine Learning Workshop, 1989, pp. 164-168.
28. V. Raghavan, D. Wilkins, A nearly-linear time equivalence test and characterization of μ-branching programs, Mathematical Systems Theory 30 (1997) 249-283.
29. D. Schuurmans, R. Greiner, Learning default concepts, in: Proceedings of the Tenth Canadian Conference on Artificial Intelligence, 1994, pp. 519-523.
30. D. Schuurmans, R. Greiner, Learning to classify incomplete examples, in: Computational Learning Theory and Natural Learning Systems, Volume IV: Making Learning Systems Practical, MIT Press, 1997, pp. 87-105 (Chapter 6).
31. L.G. Valiant, A theory of the learnable, Communications of ACM 27 (11) (1984) 1134-1142.
32. V.N. Vapnik, A.Y. Chervonenkis, On the uniform convergence of relative frequencies of events to their probabilities, Theory of Probability and its Applications 16 (2) (1971) 264-280.