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Machine Learning: ECML 2000

53,49 €*

Sofort verfügbar, Lieferzeit: 1-3 Tage

Produktnummer: 18135d2c2c62694572bf3d152aa4dff604
Themengebiete: Algorithmic Learning Classification Data Mining Decision Diagrams Inductive Learning Inductive Logic Programming Inference algorithms cognition complexity
Veröffentlichungsdatum: 17.05.2000
EAN: 9783540676027
Sprache: Englisch
Seitenzahl: 472
Produktart: Kartoniert / Broschiert
Herausgeber: Lopez de Mantaras, Ramon Plaza, Enric
Verlag: Springer Berlin
Untertitel: 11th European Conference on Machine Learning Barcelona, Catalonia, Spain May, 31 - June 2, 2000 Proceedings
Produktinformationen "Machine Learning: ECML 2000"
The biennial European Conference on Machine Learning (ECML) series is intended to provide an international forum for the discussion of the latest high quality research results in machine learning and is the major European scienti?c event in the ?eld. The eleventh conference (ECML 2000) held in Barcelona, Catalonia, Spain from May 31 to June 2, 2000, has continued this tradition by attracting high quality papers from around the world. Scientists from 21 countries submitted 100 papers to ECML 2000, from which 20 were selected for long oral presentations and 23 for short oral presentations. This selection was based on the recommendations of at least two reviewers for each submitted paper. It is worth noticing that the number of papers reporting applications of machine learning has increased in comparison to past ECML conferences. We believe this fact shows the growing maturity of the ?eld. This volume contains the 43 accepted papers as well as the invited talks by Katharina Morik from theUniversity of Dortmund and Pedro Domingos from the University of Washington at Seattle. In addition, three workshops were jointly organized by ECML 2000 and the European Network of Excellence - net: “Dealing with Structured Data in Machine Learning and Statistics W- stites”, “Machine Learning in the New Information Age” , and “Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Com- nation”.

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