By Naoki Abe, Roni Khardon, Thomas Zeugmann
This quantity comprises the papers offered on the twelfth Annual convention on Algorithmic studying idea (ALT 2001), which was once held in Washington DC, united states, in the course of November 25–28, 2001. the most goal of the convention is to supply an inter-disciplinary discussion board for the dialogue of theoretical foundations of computer studying, in addition to their relevance to useful functions. The convention was once 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 laptop studying. also, the amount comprises the invited talks of ALT 2001 offered by way of 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 provided by way of Setsuo Arikawa of Kyushu college, Japan. additionally, 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 at school Park, united states. the entire types of those papers are released within the DS 2001 lawsuits (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
For each leaf node assigned more than one concept from C, choose a membership query that partitions the set of concepts assigned to the node as evenly as possible, and extend the tree and its evaluation until every leaf node is assigned exactly one concept from C. Arkin et al. show that this method achieves a tree whose height is within a factor of log |C| of the optimal height. ) 10 XTD and MQ-Algorithms Using a specifying set S for a concept c , we can replace an equivalence query with c by a sequence of membership queries with the elements of S as follows.
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. 14. R. Hyaﬁl and R. L. Rivest. Constructing optimal binary trees is NP-complete. Information Processing Letters, 5:15–17, 1976. 15. N. Littlestone. Learning quickly when irrelevant attributes abound: A new linearthreshold algorithm. Machine Learning, 2:285–318, 1988. 16. W. Maass and G. Tur´ an. Lower bound methods and separation results for on-line learning models.
Figure 1 shows four seconds of data from a Pioneer 1 robot as it moves past an object. Prior to moving, the robot establishes a coordinate frame with an x axis perpendicular to its heading and a y axis parallel to its heading. As it begins to move, the robot measures its location in this coordinate frame. Note that the robot-x line is almost constant. This means that the robot did not change its heading as it moved. In contrast, the robot-y line increases, indicating that the robot does increase its distance along a line parallel to its original heading.
Algorithmic Learning Theory: 12th International Conference, ALT 2001 Washington, DC, USA, November 25–28, 2001 Proceedings by Naoki Abe, Roni Khardon, Thomas Zeugmann