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Ajouter au panierEtat : New. In English.
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Ajouter au panierEtat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Seiten: 660 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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Langue: anglais
Edité par Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10 : 9819920957 ISBN 13 : 9789819920952
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Ajouter au panierhardcover. Etat : New. In shrink wrap. Looks like an interesting title!
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering.
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Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMâO). EMâO algorithms, namely EMâOAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EMâOAs amenable to application of ML for different pursuits.Recognizing the immense potential for ML-based enhancements in the EMâO domain, this book intends to serve as an exclusive resource for both domain novices and the experienced researchers and practitioners.To achieve this goal, the book first covers the foundations of optimization, including problem and algorithm types.Then, well-structured chapters present some of the key studies on ML-based enhancements in the EMâO domain, systematically addressing important aspects. These include learning to understand the problem structure, converge better, diversify better, simultaneously converge and diversify better, and analyze the Pareto Front. In doing so, this book broadly summarizes the literature, beginning with foundational work on innovization (2003) and objective reduction (2006), and extending to the most recently proposed innovized progress operators (2021-23). It also highlights the utility of ML interventions in the search, post-optimality, and decision-making phases pertaining to the use of EMâOAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMâOA domain.To aid readers, the book includes working codes for the developed algorithms. This book will not only strengthen this emergent theme but also encourage ML researchers to develop more efficient and scalable methods that cater to the requirements of the EMâOA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EMâOA and ML domains.
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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Seiten: 676 | Sprache: Englisch | Produktart: Bücher | Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
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Ajouter au panierTaschenbuch. Etat : Neu. Multi-Objective Machine Learning | Yaochu Jin | Taschenbuch | xiv | Englisch | 2010 | Springer | EAN 9783642067969 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Langue: anglais
Edité par Springer-Nature New York Inc, 2024
ISBN 10 : 9819920957 ISBN 13 : 9789819920952
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Ajouter au panierHardcover. Etat : Brand New. 259 pages. 9.25x6.10x9.21 inches. In Stock.
Langue: anglais
Edité par Springer Berlin Heidelberg, 2006
ISBN 10 : 3540306765 ISBN 13 : 9783540306764
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Ajouter au panierGebunden. Etat : New. Selected collection of recent research on multi-objective approach to machine learningRecent developments in evolutionary multi-objective optimizationApplies the concept of Pareto-optimality to machine learning Recently.
Langue: anglais
Edité par Springer Berlin Heidelberg, 2010
ISBN 10 : 3642067964 ISBN 13 : 9783642067969
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Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
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Ajouter au panierEtat : Used. pp. 676.
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Ajouter au panierEtat : Used. pp. 676 Illus.
Langue: anglais
Edité par Springer Berlin Heidelberg, 2010
ISBN 10 : 3642067964 ISBN 13 : 9783642067969
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Ajouter au panierPaperback. Etat : Brand New. 660 pages. 9.25x6.10x1.53 inches. In Stock.
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Ajouter au panierPaperback. Etat : Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Ajouter au panierEtat : Used. pp. 676.
Langue: anglais
Edité par Springer, Berlin, Springer, 2006
ISBN 10 : 3540306765 ISBN 13 : 9783540306764
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Ajouter au panierBuch. Etat : Neu. Neuware - Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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Ajouter au panierHardcover. Etat : Like New. Like New. book.
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Ajouter au panierEtat : new. Questo è un articolo print on demand.