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 concept (ALT 2001), which was once held in Washington DC, united states, in the course of November 25–28, 2001. the most target of the convention is to supply an inter-disciplinary discussion board for the dialogue of theoretical foundations of desktop studying, in addition to their relevance to useful functions. The convention used to be co-located with the Fourth overseas convention on Discovery technological know-how (DS 2001). the amount contains 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 concept and perform of desktop studying. also, the amount comprises the invited talks of ALT 2001 offered via Dana Angluin of Yale collage, united states, Paul R. Cohen of the collage of Massachusetts at Amherst, united states, and the joint invited speak for ALT 2001 and DS 2001 offered by way of Setsuo Arikawa of Kyushu college, Japan. additionally, this quantity comprises abstracts of the invited talks for DS 2001 provided via Lindley Darden and Ben Shneiderman either one of the college of Maryland in school Park, united states. the total types of those papers are released within the DS 2001 court cases (Lecture Notes in Arti?cial Intelligence Vol. 2226).

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15,5]). Another difficulty with naive notions of denotation is referential ambiguity. Does the word “cup” refer to an object, the shape of the object, its color, the actions one performs on it, the spatial relationship between it and another object, or some other feature of the episode in which the word is uttered? How can an algorithm learn the denotation of a word when so many denotations are logically possible? Let us illustrate Oates’ approach to these problems with the word “square,” which has a relatively easy denotation.

Ehrenfeucht, D. Haussler, M. Kearns, and L. Valiant. A general lower bound on the number of examples needed for learning. Inform. , 82:247–261, 1989. 9. R. Gavald` a. On the power of equivalence queries. In EUROCOLT: European Conference on Computational Learning Theory, pages 193–203. Clarendon Press, 1993. 10. S. A. Goldman and M. J. Kearns. On the complexity of teaching. J. of Comput. Syst. , 50:20–31, 1995. 11. Y. Hayashi, S. Matsumoto, A. Shinohara, and M. Takeda. Uniform characterizations of polynomial-query learnabilities.

Examples. To get a sense of the difference between the exclusion dimension and the sample exclusion dimension, consider the concept class C1 , presented in Figure 7. This is a version of addressing, described by Maass and Tur´ an [16]. The empty set is not an element of C1 , but it has a specifying set {x1 , x2 }, because only c1 also does not include either x1 or x2 . However, the sample s = {(y1 , 0), (y2 , 0), (y3 , 0), (y4 , 0)}, Queries Revisited c1 c2 c3 c4 x1 x2 y1 y2 y3 y4 0 0 1 1 0 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 27 Fig.

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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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