By Naoki Abe, Roni Khardon, Thomas Zeugmann
This quantity includes the papers awarded on the twelfth Annual convention on Algorithmic studying conception (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 sensible purposes. The convention was once co-located with the Fourth foreign convention on Discovery technology (DS 2001). the quantity contains 21 contributed papers. those papers have been chosen via this system committee from forty two submissions in keeping 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 provided by means of Dana Angluin of Yale collage, 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 by way of Setsuo Arikawa of Kyushu college, Japan. in addition, this quantity contains abstracts of the invited talks for DS 2001 provided by means of Lindley Darden and Ben Shneiderman either one of the collage of Maryland in school Park, united states. the whole models of those papers are released within the DS 2001 complaints (Lecture Notes in Arti?cial Intelligence Vol. 2226).
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Extra info for Algorithmic Learning Theory: 12th International Conference, ALT 2001 Washington, DC, USA, November 25–28, 2001 Proceedings
L. G. Valiant. A theory of the learnable. Commun. ACM, 27:1134–1142, 1984. Robot Baby 2001 Paul R. Cohen1 , Tim Oates2 , Niall Adams3 , and Carole R. edu Abstract. In this paper we claim that meaningful representations can be learned by programs, although today they are almost always designed by skilled engineers. We discuss several kinds of meaning that representations might have, and focus on a functional notion of meaning as appropriate for programs to learn. Speciﬁcally, a representation is meaningful if it incorporates an indicator of external conditions and if the indicator relation informs action.
In Proceedings of the International School-Seminar on Discrete Mathematics and Mathematical Cybernetics, pages 6–10. MAX Press, Moscow, 2001. 18. A. Shinohara and S. Miyano. Teachability in computational learning. New Generation Computing, 8(4):337–348, 1991. 19. L. G. Valiant. A theory of the learnable. Commun. ACM, 27:1134–1142, 1984. Robot Baby 2001 Paul R. Cohen1 , Tim Oates2 , Niall Adams3 , and Carole R. edu Abstract. In this paper we claim that meaningful representations can be learned by programs, although today they are almost always designed by skilled engineers.
If c ∈ C, then we take a minimum specifying set S for c with 24 D. Angluin respect to C and replace the XEQ by MQ’s about the elements of S, as described in Section 10. Using the standard optimal algorithm instead of the halving algorithm gives the following. #MQ&EQ(C) ≤ (XD(C)) · (#XEQ(C)). (27) Again Heged¨ us improves the upper bound of (26) by making a more careful choice of the ordering of MQ’s, and gives an example of a family of classes for which the improved bound is asymptotically tight.
Algorithmic Learning Theory: 12th International Conference, ALT 2001 Washington, DC, USA, November 25–28, 2001 Proceedings by Naoki Abe, Roni Khardon, Thomas Zeugmann