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Añadir al carritoTaschenbuch. Condición: Neu. Complex Hybrid Sets and their Applications in Decision Making | Theory and Applications | Chinnadurai V (u. a.) | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206159476 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Añadir al carritoTaschenbuch. Condición: Neu. Complex Decision Making | Theory and Practice | Hassan Qudrat-Ullah (u. a.) | Taschenbuch | Understanding Complex Systems | xii | Englisch | 2010 | Springer | EAN 9783642092831 | 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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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Today's ever more complex world creates challenges for decision makers. This volume reviews the principles underlying complex decision making, the handling of uncertainties in dynamic environments, and the various modeling approaches. Beginning with a discussion of the underlying concepts, theories and empirical evidence, the book gives you a range of practical tools and techniques for decision making in complex environments and systems.
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Today's ever more complex world creates challenges for decision makers. This volume reviews the principles underlying complex decision making, the handling of uncertainties in dynamic environments, and the various modeling approaches. Beginning with a discussion of the underlying concepts, theories and empirical evidence, the book gives you a range of practical tools and techniques for decision making in complex environments and systems.
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis methods. This dissertation contributes a nascent design theory, named the Division-of-Labor framework, for developing complex machine learning systems that can not only address the challenges of big data but also leverage their characteristics to perform more sophisticated analyses. I evaluate the proposed design principles in three practical settings, in which I apply the principles to design machine learning systems that (i) support treatment decision making for cancer patients, (ii) provide consumers with recommendations on two-sided platforms, and (iii) address a trade-off between efficiency and comfort in the context of autonomous vehicles. The evaluations partially validate the proposed theory, but also show that some principles require further attention in order to be practicable. 202 pp. Englisch.
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnThe ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis m.
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis methods. This dissertation contributes a nascent design theory, named the Division-of-Labor framework, for developing complex machine learning systems that can not only address the challenges of big data but also leverage their characteristics to perform more sophisticated analyses. I evaluate the proposed design principles in three practical settings, in which I apply the principles to design machine learning systems that (i) support treatment decision making for cancer patients, (ii) provide consumers with recommendations on two-sided platforms, and (iii) address a trade-off between efficiency and comfort in the context of autonomous vehicles. The evaluations partially validate the proposed theory, but also show that some principles require further attention in order to be practicable.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 202 pp. Englisch.
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The ubiquitousness of data and the emergence of data-driven machine learning approaches provide new means of creating insights. However, coping with the great volume, velocity, and variety of data requires improved data analysis methods. This dissertation contributes a nascent design theory, named the Division-of-Labor framework, for developing complex machine learning systems that can not only address the challenges of big data but also leverage their characteristics to perform more sophisticated analyses. I evaluate the proposed design principles in three practical settings, in which I apply the principles to design machine learning systems that (i) support treatment decision making for cancer patients, (ii) provide consumers with recommendations on two-sided platforms, and (iii) address a trade-off between efficiency and comfort in the context of autonomous vehicles. The evaluations partially validate the proposed theory, but also show that some principles require further attention in order to be practicable.
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Añadir al carritoTaschenbuch. Condición: Neu. Towards a Theory for Designing Machine Learning Systems for Complex Decision Making Problems | Schahin Tofangchi | Taschenbuch | Göttinger Wirtschaftsinformatik | 202 S. | Englisch | 2020 | Cuvillier | EAN 9783736972001 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu Print on Demand.
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Publicado por LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206159477 ISBN 13: 9786206159476
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A number of methods like Linear programming, Dynamic programming, Assignment problem, Transportation problem, Game theory etc. are examples of decision making under certainty. When there are uncertainties or vagueness in the data regarding alternatives and /or criteria, we use different techniques for decision making based on whether the data involves probabilities, fuzzy sets, soft sets, rough sets, neutrosophic sets or any other hybrid structure. Decision problem involving the evaluation of multiple criteria and alternatives are called multi-criteria decision making (MCDM) problem. The methodologies studied in the domain of multi-criteria decision making are also considered as a subset of operation research that deals with structuring, planning and solving decision making problems that involve multiple criteria and/or multiple alternatives with the purpose of helping decision makers involved in such processes. This book is the outcome of the research work carried out by the authors towards finding application of complex cubic set, complex cubic intuitionistic fuzzy set and other complex hybrid sets.