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Editore: Springer Berlin Heidelberg, 2008
ISBN 10: 3642096158 ISBN 13: 9783642096150
Lingua: Inglese
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases | Ashish Ghosh (u. a.) | Taschenbuch | xiv | Englisch | 2010 | Springer Vieweg | EAN 9783642096150 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Mär 2008, 2008
ISBN 10: 3540774661 ISBN 13: 9783540774662
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware -Data Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdisciplinary fields only a few of them are well equipped to handle these multi-criteria issues of DM. Therefore, the DM issues have attracted considerable attention of the well established multiobjective genetic algorithm community to optimize the objectives in the tasks of DM.The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 176 pp. Englisch.
Editore: Springer Berlin Heidelberg, 2010
ISBN 10: 3642096158 ISBN 13: 9783642096150
Lingua: Inglese
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Data Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdisciplinary fields only a few of them are well equipped to handle these multi-criteria issues of DM. Therefore, the DM issues have attracted considerable attention of the well established multiobjective genetic algorithm community to optimize the objectives in the tasks of DM.The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.
Editore: Springer Berlin Heidelberg, 2008
ISBN 10: 3540774661 ISBN 13: 9783540774662
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 106,99
Quantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Data Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdisciplinary fields only a few of them are well equipped to handle these multi-criteria issues of DM. Therefore, the DM issues have attracted considerable attention of the well established multiobjective genetic algorithm community to optimize the objectives in the tasks of DM.The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.
Da: Mispah books, Redhill, SURRE, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Like New. Like New. book.
Editore: Springer Berlin Heidelberg Mrz 2008, 2008
ISBN 10: 3540774661 ISBN 13: 9783540774662
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 106,99
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases. 176 pp. Englisch.
Editore: Springer Berlin Heidelberg Nov 2010, 2010
ISBN 10: 3642096158 ISBN 13: 9783642096150
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 106,99
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases. 176 pp. Englisch.
Editore: Springer Berlin Heidelberg, 2010
ISBN 10: 3642096158 ISBN 13: 9783642096150
Lingua: Inglese
Da: moluna, Greven, Germania
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Assembles high quality original contributions that reflect and advance the state-of-the art in the area of Multi-objective Evolutionary Algorithms for Data Mining and Knowledge DiscoveryEmphasizes on the utility of evolutionary algorithms to vario.
Editore: Springer Berlin Heidelberg, 2008
ISBN 10: 3540774661 ISBN 13: 9783540774662
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 92,27
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Assembles high quality original contributions that reflect and advance the state-of-the art in the area of Multi-objective Evolutionary Algorithms for Data Mining and Knowledge DiscoveryEmphasizes on the utility of evolutionary algorithms to vario.
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 176 67 Illus.
Da: Biblios, Frankfurt am main, HESSE, Germania
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 176.
Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2010, 2010
ISBN 10: 3642096158 ISBN 13: 9783642096150
Lingua: Inglese
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 106,99
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdisciplinary fields only a few of them are well equipped to handle these multi-criteria issues of DM. Therefore, the DM issues have attracted considerable attention of the well established multiobjective genetic algorithm community to optimize the objectives in the tasks of DM.The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 176 pp. Englisch.