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Download PDF by Naoki Abe, Roni Khardon, Thomas Zeugmann: Algorithmic Learning Theory: 12th International Conference,

By Naoki Abe, Roni Khardon, Thomas Zeugmann

ISBN-10: 3540428755

ISBN-13: 9783540428756

ISBN-10: 3540455833

ISBN-13: 9783540455837

This quantity includes the papers awarded on the twelfth Annual convention on Algorithmic studying conception (ALT 2001), which used to be held in Washington DC, united states, in the course of November 25–28, 2001. the most aim of the convention is to supply an inter-disciplinary discussion board for the dialogue of theoretical foundations of laptop studying, in addition to their relevance to functional purposes. The convention used to be co-located with the Fourth foreign convention on Discovery technology (DS 2001). the quantity comprises 21 contributed papers. those papers have been chosen by way of this system committee from forty two submissions in line with readability, signi?cance, o- ginality, and relevance to conception and perform of computer studying. also, the quantity comprises the invited talks of ALT 2001 offered by way of Dana Angluin of Yale college, united states, Paul R. Cohen of the college of Massachusetts at Amherst, united states, and the joint invited speak for ALT 2001 and DS 2001 offered through Setsuo Arikawa of Kyushu college, Japan. in addition, this quantity contains abstracts of the invited talks for DS 2001 offered via Lindley Darden and Ben Shneiderman either one of the collage of Maryland in school Park, united states. the entire models of those papers are released within the DS 2001 complaints (Lecture Notes in Arti?cial Intelligence Vol. 2226).

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Additional info for Algorithmic Learning Theory: 12th International Conference, ALT 2001 Washington, DC, USA, November 25–28, 2001 Proceedings

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The extended teaching dimension of a concept class C, denoted XTD(C), is the maximum over all concepts c ⊆ X, of the minimum size of any specifying set for c with respect to C. Clearly, for any concept class C, XTD(C) = max{TD(C), XD(C)}. (20) From (16) and (18) we have XTD(C) = max min c∈2X T ∈TM Q (C) d(c, T ). (21) 22 D. Angluin From (17) and (19), we have XTD(C) ≤ #MQ(C). (22) Examples. XTD(C0 ) = 2 = max{2, 1}. If |X| ≥ 2, XTD(S(X)) = |X| − 1 and XTD(S + (X)) = |X|. 9 The Testing Perspective In the simplest testing framework there is an unknown item, for example, a disease, and a number of possible binary tests to perform to try to identify the unknown item.

Castro, D. -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. 7. A. Blumer, A. Ehrenfeucht, D. Haussler, and M. K. Warmuth. Learnability and the Vapnik-Chervonenkis dimension. J. ACM, 36:929–965, 1989. 8. A. Ehrenfeucht, D. Haussler, M. Kearns, and L. Valiant. A general lower bound on the number of examples needed for learning.

MQ(C) ≤ (2XTD(C)/(log XTD(C))) · ( log |C| ). (25) He also gives an example of a family of concept classes for which this improved bound is asymptotically tight. These results give a reasonably satisfying characterization of the number of membership queries needed to learn a concept class C in terms of a combinatorial parameter of the class, the extended teaching dimension, XTD(C). The factor of roughly log |C| difference between the lower bound and the upper bound may be thought of as tolerably small, being the number of bits needed to name all the concepts in C.

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Algorithmic Learning Theory: 12th International Conference, ALT 2001 Washington, DC, USA, November 25–28, 2001 Proceedings by Naoki Abe, Roni Khardon, Thomas Zeugmann

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