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Ajouter au panierHardcover. Etat : Very Good. 1. Auflage. Unread, some shelfwear. Immediately dispatched from Germany.
Langue: anglais
Edité par VDM Verlag Dr. Müller e.K., 2013
ISBN 10 : 383648465X ISBN 13 : 9783836484657
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Ajouter au panierTaschenbuch. Etat : Neu. Evolving Fuzzy Models | Incremental Learning, Interpretability and Stability Issues, Applications | Edwin Lughofer | Taschenbuch | 156 S. | Englisch | 2013 | VDM Verlag Dr. Müller e.K. | EAN 9783836484657 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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Ajouter au panierPaperback. Etat : Brand New. 156 pages. 8.66x5.91x0.36 inches. In Stock.
Langue: anglais
Edité par Berlin ; Heidelberg : Springer, 2011
ISBN 10 : 3642180868 ISBN 13 : 9783642180866
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Ajouter au panierOPp., gebundene Ausgabe. Etat : Gut. XXIV, 454 S.: graph. Darst. ; 24 cm, Cover with little wear, good condition. ISBN: 9783642180866 Altersfreigabe FSK ab 0 Jahre Sprache: Englisch Gewicht in Gramm: 1050.
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Ajouter au panierTaschenbuch. Etat : Neu. Learning in Non-Stationary Environments | Methods and Applications | Edwin Lughofer (u. a.) | Taschenbuch | xii | Englisch | 2014 | Springer US | EAN 9781489993403 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Seiten: 480 | Sprache: Englisch | Produktart: Bücher | In today¿s real-world applications, there is an increasing demand of integrating new information and knowledge on-demand into model building processes to account for changing system dynamics, new operating conditions, varying human behaviors or environmental influences.Evolving fuzzy systems (EFS) are a powerful tool to cope with this requirement, as they are able to automatically adapt parameters, expand their structure and extend their memory on-the-fly, allowing on-line/real-time modeling. This book comprises several evolving fuzzy systems approaches which have emerged during the last decade and highlights the most important incremental learning methods used. The second part is dedicated to advanced concepts for increasing performance, robustness, process-safety and reliability, for enhancing user-friendliness and enlarging the field of applicability of EFS and for improving the interpretability and understandability of the evolved models. The third part underlines the usefulness and necessity of evolving fuzzy systems in several online real-world application scenarios, provides an outline of potential future applications and raises open problems and new challenges for the next generation evolving systems, including human-inspired evolving machines. The book includes basic principles, concepts, algorithms and theoretic results underlined by illustrations. It is dedicated to researchers from the field of fuzzy systems, machine learning, data mining and system identification as well as engineers and technicians who apply data-driven modeling techniques in real-world systems.
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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Seiten: 480 | Sprache: Englisch | Produktart: Bücher | In today¿s real-world applications, there is an increasing demand of integrating new information and knowledge on-demand into model building processes to account for changing system dynamics, new operating conditions, varying human behaviors or environmental influences.Evolving fuzzy systems (EFS) are a powerful tool to cope with this requirement, as they are able to automatically adapt parameters, expand their structure and extend their memory on-the-fly, allowing on-line/real-time modeling. This book comprises several evolving fuzzy systems approaches which have emerged during the last decade and highlights the most important incremental learning methods used. The second part is dedicated to advanced concepts for increasing performance, robustness, process-safety and reliability, for enhancing user-friendliness and enlarging the field of applicability of EFS and for improving the interpretability and understandability of the evolved models. The third part underlines the usefulness and necessity of evolving fuzzy systems in several online real-world application scenarios, provides an outline of potential future applications and raises open problems and new challenges for the next generation evolving systems, including human-inspired evolving machines. The book includes basic principles, concepts, algorithms and theoretic results underlined by illustrations. It is dedicated to researchers from the field of fuzzy systems, machine learning, data mining and system identification as well as engineers and technicians who apply data-driven modeling techniques in real-world systems.
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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Seiten: 452 | Sprache: Englisch | Produktart: Bücher | Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research. .
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Langue: anglais
Edité par Springer New York, Springer US, 2012
ISBN 10 : 1441980199 ISBN 13 : 9781441980199
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.
Langue: anglais
Edité par Springer Berlin Heidelberg, 2011
ISBN 10 : 3642180868 ISBN 13 : 9783642180866
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Ajouter au panierGebunden. Etat : New.
Langue: anglais
Edité par Springer Berlin Heidelberg, 2013
ISBN 10 : 3642266924 ISBN 13 : 9783642266928
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Langue: anglais
Edité par Springer New York, Springer New York, 2014
ISBN 10 : 1489993401 ISBN 13 : 9781489993403
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.