Algorithmic Learning Theory: 13th International Conference, by Nicolò Cesa-Bianchi, Masayuki Numao, Rüdiger Reischuk (eds.)

By Nicolò Cesa-Bianchi, Masayuki Numao, Rüdiger Reischuk (eds.)

This quantity includes the papers awarded on the thirteenth Annual convention on Algorithmic studying conception (ALT 2002), which used to be held in Lub ¨ eck (Germany) in the course of November 24–26, 2002. the most goal of the convention used to be to p- vide an interdisciplinary discussion board discussing the theoretical foundations of desktop studying in addition to their relevance to functional functions. The convention used to be colocated with the 5th foreign convention on Discovery technology (DS 2002). the amount contains 26 technical contributions that have been chosen by means of this system committee from forty nine submissions. It additionally includes the ALT 2002 invited talks provided by way of Susumu Hayashi (Kobe collage, Japan) on “Mathematics in response to Learning”, through John Shawe-Taylor (Royal Holloway college of L- don, united kingdom) on “On the Eigenspectrum of the Gram Matrix and Its dating to the Operator Eigenspectrum”, and through Ian H. Witten (University of Waikato, New Zealand) on “Learning constitution from Sequences, with purposes in a electronic Library” (joint invited speak with DS 2002). moreover, this quantity - cludes abstracts of the invited talks for DS 2002 provided by way of Gerhard Widmer (Austrian learn Institute for Arti?cial Intelligence, Vienna) on “In seek of the Horowitz issue: period in-between file on a Musical Discovery undertaking” and by way of Rudolf Kruse (University of Magdeburg, Germany) on “Data Mining with Graphical Models”. the whole types of those papers are released within the DS 2002 court cases (Lecture Notes in Arti?cial Intelligence, Vol. 2534). ALT has been awarding the E.

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At Abstract. The paper gives an overview of an inter-disciplinary research project whose goal is to elucidate the complex phenomenon of expressive music performance with the help of machine learning and automated discovery methods. The general research questions that guide the project are laid out, and some of the most important results achieved so far are briefly summarized (with an emphasis on the most recent and still very speculative work). A broad view of the discovery process is given, from data acquisition issues through data visualization to inductive model building and pattern discovery.

On the method of bounded differences. In Surveys in Combinatorics 1989, pages 148–188. Cambridge University Press, 1989. 40 J. Shawe-Taylor et al. [13] S. Mika, B. Sch¨ olkopf, A. -R. M¨ uller, M. Scholz, and G. R¨ atsch. Kernel PCA and de-noising in feature spaces. In Advances in Neural Information Processing Systems 11, 1998. [14] Andrew Y. Ng, Alice X. Zheng, and Michael I. Jordan. Link analysis, eigenvectors and stability. In To appear in the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), 2001.

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