By Naoki Abe, Roni Khardon, Thomas Zeugmann
This quantity comprises the papers offered on the twelfth Annual convention on Algorithmic studying thought (ALT 2001), which was once 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 sensible purposes. The convention was once co-located with the Fourth foreign convention on Discovery technological know-how (DS 2001). the quantity comprises 21 contributed papers. those papers have been chosen by way of this system committee from forty two submissions in accordance with readability, signi?cance, o- ginality, and relevance to conception and perform of laptop studying. also, the amount comprises the invited talks of ALT 2001 awarded via Dana Angluin of Yale college, 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 awarded by way of Setsuo Arikawa of Kyushu college, Japan. in addition, this quantity comprises abstracts of the invited talks for DS 2001 offered by means of Lindley Darden and Ben Shneiderman either one of the collage of Maryland in school Park, united states. the total 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
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. There is a ﬁnite binary relation between the possible items and the possible tests; performing a test on the unknown item is analogous to a membership query, and adaptive testing algorithms correspond to MQalgorithms. Hence the applicability of Moshkov’s results on testing to questions about MQ-algorithms.
Suppose we wanted to talk to the robot about cups: We would say, “there’s a cup” when we see it looking at a cup; or, “a cup is on your left,” when a cup is outside its ﬁeld of view; or, “point to the cup,” when there are several objects in view, and so on. To learn the meaning of the word “cup” the robot must ﬁrst individuate the word in the speech signal, then individuate the object “cup” in other sensory series, associate the representations; and perhaps estimate some properties of the object corresponding to the cup, such as its color, or the fact that it participates as a target in a “turn toward” activity.
The VC-dimension of a class C of concepts, Queries Revisited 29 denoted VCD(C), is the maximum cardinality of any set shattered by C. It is clear that for any concept class C, This and (3) imply VCD(C) ≤ log |C|. (35) VCD(C) ≤ #MQ(C). (36) As Littlestone  observed, an adversary giving counterexamples from a shattered set can enforce VCD(C) XEQ’s, and therefore VCD(C) ≤ #XEQ(C) ≤ #EQ(C). (37) Maass and Tur´ an  show that for any concept class C, 1 VCD(C) ≤ #MQ&EQ(C). 41, and also show that 1 VCD(C) ≤ #MQ&XEQ(C).
Algorithmic Learning Theory: 12th International Conference, ALT 2001 Washington, DC, USA, November 25–28, 2001 Proceedings by Naoki Abe, Roni Khardon, Thomas Zeugmann