By Naoki Abe, Roni Khardon, Thomas Zeugmann

This quantity comprises the papers awarded on the twelfth Annual convention on Algorithmic studying thought (ALT 2001), which used to be 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 computing device 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 amount comprises 21 contributed papers. those papers have been chosen via this system committee from forty two submissions in response to readability, signi?cance, o- ginality, and relevance to concept and perform of laptop studying. also, the quantity includes the invited talks of ALT 2001 provided by way 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 awarded by means of Setsuo Arikawa of Kyushu collage, Japan. additionally, this quantity contains abstracts of the invited talks for DS 2001 provided by means of Lindley Darden and Ben Shneiderman either one of the college of Maryland at school Park, united states. the full models of those papers are released within the DS 2001 lawsuits (Lecture Notes in Arti?cial Intelligence Vol. 2226).

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

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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. In Proceedings of the 1st International Conference on Discovery Science (DS-98), volume 1532 of LNAI, pages 84–92, 1998.

We’ll need samples as well as concepts. A sample s is a partial function from X to {0, 1}. ” If we identify a concept c with its characteristic function, mapping X to {0, 1}, then a concept is a special case of a sample. Two samples are consistent if they take the same values on the elements common to both of their domains. A sample s extends a sample s if they are consistent and the domain of s is a subset of the domain of s . It is interesting to note that the partial equivalence queries of Maass and Tur´ an [16] can be characterized as equivalence queries with samples instead of just concepts.

2. A dynamic time warping algorithm compares every pair of episodes and returns a number that represents the degree of similarity between the time series in the pair. Dynamic time warping is a technique for “morphing” one multivariate time series into another by stretching and compressing the horizontal (temporal) axis of one series relative to the other [13]. The algorithm returns a degree of mismatch (conversely, similarity) between the series after the best fit between them has been found. 3.

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