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 the...
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| Další autoři: | , , , , |
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| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Vydání: | 1st ed. 2009. |
| Edice: | Studies in Computational Intelligence
201 |
| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Poznámka: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Foundations of computational intelligence, Volume 1, Learning and approximation, Aboul-Ella Hassanien, Ajith Abraham, Athanasios V. Vasilakos and Witold Pedrycz (eds), Berlin Heidelberg, Springer, 2009, 1 vol. (XII-397 p.), Studies in computational intelligence, 978-3-642-01081-1 • Foundations of Computational Intelligence, Texte imprimé, 9783642010835 • Foundations of computational intelligence, Volume 1, Learning and approximation, Aboul-Ella Hassanien, Ajith Abraham, Athanasios V. Vasilakos and Witold Pedrycz (eds), Berlin Heidelberg, Springer, 2009, 1 vol. (XII-397 p.), Studies in computational intelligence, 978-3-642-01081-1 • Foundations of Computational Intelligence, Texte imprimé, 9783662568439 |
| Shrnutí: | 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, scheduling problems and so on. The past years have witnessed a large number of interesting applications using various techniques of Computational Intelligence such as rough sets, connectionist learning; fuzzy logic; evolutionary computing; artificial immune systems; swarm intelligence; reinforcement learning, intelligent multimedia processing etc.. In spite of numerous successful applications of Computational Intelligence in business and industry, it is sometimes difficult to explain the performance of these techniques and algorithms from a theoretical perspective. Therefore, we encouraged authors to present original ideas dealing with the incorporation of different mechanisms of Computational Intelligent dealing with Learning and Approximation algorithms and underlying processes. This edited volume comprises 15 chapters, including an overview chapter, which provides an up-to-date and state-of-the art research on the application of Computational Intelligence for learning and approximation |
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| Popis jednotky: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783642010828 |
| ISSN: | 1860-9503 |
| Přístup: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |