Algorithms — ESA '97: 5th Annual European Symposium Graz, by A. K. Amoura, E. Bampis, C. Kenyon, Y. Manoussakis (auth.),

Algorithms — ESA '97: 5th Annual European Symposium Graz, by A. K. Amoura, E. Bampis, C. Kenyon, Y. Manoussakis (auth.),

By A. K. Amoura, E. Bampis, C. Kenyon, Y. Manoussakis (auth.), Rainer Burkard, Gerhard Woeginger (eds.)

This ebook constitutes the refereed lawsuits of the fifth Annual foreign ecu Symposium on Algorithms, ESA'97, held in Graz, Austria, September 1997.
The 38 revised complete papers awarded have been chosen from 112 submitted papers. The papers tackle a wide spectrum of theoretical and applicational elements in algorithms thought and layout. one of the subject matters lined are approximation algorithms, graph and community algorithms, combinatorial optimization, computational biology, computational arithmetic, facts compression, disbursed computing, evolutionary algorithms, neural computing, on-line algorithms, parallel computing, development matching, and others.

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Extra info for Algorithms — ESA '97: 5th Annual European Symposium Graz, Austria, September 15–17, 1997 Proceedings

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The question of how a user (or teacher) may best select examples to help a learner identify a target concept is the focus of much work in computational learning theory. See Goldman and Kearns [12] for a detailed treatment of the problem. The Scatter/Gather algorithm [6] is an interactive clustering algorithm designed for information retrieval. The system provides an initial clustering of data. When the user selects a subset of the clusters for further examination, the system gathers their components and regroups them to form new clusters.

Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society Series B (Methodological), 39(1):1–38, 1977.

2: Learning curves for supervised, unsupervised, and semisupervised clustering. For supervised clustering, cluster purity (measured on the train set) and generalization (measured on an independent test set) are plotted against the number of labeled examples; for semi-supervised clustering, purity is plotted against the number of constraints. Averages over 10 runs each, with the upper and lower lines indicating error bars at one standard deviation. See text for details. teresting to note that the performance of the semi-supervised learner actually begins to decrease after roughly 20 constraints have been added.

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