Foundations of Computational Intelligence
Volume 1: Learning and Approximation
Learning methods and approximation algorithms are fundamental tools that deal with computationally hard problems and problems in which the input is gradually disclosed over time. Both kinds of problems have a large number of applications arising from a variety of fields, such as algorithmic game theory, approximation classes, coloring and partitioning, competitive analysis, computational finance, cuts and connectivity, geometric problems, inapproximability results, mechanism design, network design, packing and covering, paradigms for design and analysis of approximation and online algorithms, randomization techniques, real-world applications,…
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Produktdetails
Weitere Autoren: Abraham, Ajith (Hrsg.) / Vasilakos, Athanasios V. (Hrsg.) / Pedrycz, Witold (Hrsg.)
- ISBN: 978-3-642-01081-1
- EAN: 9783642010811
- Produktnummer: 31959954
- Verlag: Springer Nature EN
- Sprache: Englisch
- Erscheinungsjahr: 2009
- Seitenangabe: 400 S.
- Masse: H23.5 cm x B15.5 cm 1'660 g
- Abbildungen: s/w. Tabellen
- Reihenbandnummer: 201
- Gewicht: 1660
- Sonstiges: Research
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