Abstract
We begin with a brief tutorial on the problem of learning a finite concept class over a finite domain using membership queries and/or equivalence queries. We then sketch general results on the number of queries needed to learn a class of concepts, focusing on the various notions of combinatorial dimension that have been employed, including the teaching dimension, the exclusion dimension, the extended teaching dimension, the fingerprint dimension, the sample exclusion dimension, the Vapnik-Chervonenkis dimension, the abstract identification dimension, and the general dimension.
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D. Angluin. Queries and concept learning. Machine Learning, 2:319–342, 1988.
D. Angluin. Negative results for equivalence queries. Machine Learning, 5:121–150, 1990.
E. M. Arkin, H. Meijer, J. S. B. Mitchell, D. Rappaport, and S. S. Skiena. Decision trees for geometric models. In Proceedings of the Ninth Annual Symposium on Computational Geometry, pages 369–378, San Diego, CA, 1993. ACM Press.
J. L. Balcázar, J. Castro, and D. Guijarro. Abstract combinatorial characterizations of exact learning via queries. In Proceedings of the 13th Annual Conference on Computational Learning Theory, pages 248–254. Morgan Kaufmann, San Francisco, 2000.
J. L. Balcázar, J. Castro, and D. Guijarro. A general dimension for exact learning. In Proceedings of the 14th Annual Conference on Computational Learning Theory, 2001.
J. L. Balcázar, J. Castro, D. Guijarro, and H.-U. Simon. The consistency dimension and distribution-dependent learning from queries. In Proceedings of the 10th International Conference on Algorithic Learning Theory-ALT’ 99, volume 1720 of LNAI, pages 77–92. Springer-Verlag, 1999.
A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. Learnability and the Vapnik-Chervonenkis dimension. J. ACM, 36:929–965, 1989.
A. Ehrenfeucht, D. Haussler, M. Kearns, and L. Valiant. A general lower bound on the number of examples needed for learning. Inform. Comput., 82:247–261, 1989.
R. Gavaldà . On the power of equivalence queries. In EUROCOLT: European Conference on Computational Learning Theory, pages 193–203. Clarendon Press, 1993.
S. A. Goldman and M. J. Kearns. On the complexity of teaching. J. of Comput. Syst. Sci., 50:20–31, 1995.
Y. Hayashi, S. Matsumoto, A. Shinohara, and M. Takeda. Uniform characterizations of polynomial-query learnabilities. In Proceedings of the 1st International Conference on Discovery Science (DS-98), volume 1532of LNAI, pages 84–92, 1998.
T. Hegedüs. Generalized teaching dimensions and the query complexity of learning. In Proceedings of the 8th Annual Conference on Computational Learning Theory, pages 108–117. ACM Press, New York, NY, 1995.
L. Hellerstein, K. Pillaipakkamnatt, V. Raghavan, and D. Wilkins. How many queries are needed to learn? In Proceedings of the Twenty-Seventh Annual ACM Symposium on the Theory of Computing, pages 190–199, 1995.
R. Hyafil and R. L. Rivest. Constructing optimal binary trees is NP-complete. Information Processing Letters, 5:15–17, 1976.
N. Littlestone. Learning quickly when irrelevant attributes abound: A new linearthreshold algorithm. Machine Learning, 2:285–318, 1988.
W. Maass and G. Turán. Lower bound methods and separation results for on-line learning models. Machine Learning, 9:107–145, 1992.
M. Moshkov. Test theory and problems of machine learning. In Proceedings of the International School-Seminar on Discrete Mathematics and Mathematical Cybernetics, pages 6–10. MAX Press, Moscow, 2001.
A. Shinohara and S. Miyano. Teachability in computational learning. New Generation Computing, 8(4):337–348, 1991.
L. G. Valiant. A theory of the learnable. Commun. ACM, 27:1134–1142, 1984.
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Angluin, D. (2001). Queries Revisited. In: Abe, N., Khardon, R., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2001. Lecture Notes in Computer Science(), vol 2225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45583-3_3
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DOI: https://doi.org/10.1007/3-540-45583-3_3
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