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Multi-Objective Decision Making (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Multi-Objective Decision Making (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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Multi-Objective Decision Making (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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Multi-Objective Decision Making (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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Multi-Objective Decision Making (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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Multi-Objective Machine Learning
Published by Springer-Verlag GmbH, 2006
ISBN 10: 3540306765 ISBN 13: 9783540306764
Language: English
£ 76.18
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Multi-Objective Machine Learning
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642067964 ISBN 13: 9783642067969
Language: English
£ 131.29
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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization
Published by Springer, Berlin|Springer Nature Singapore|Springer, 2023
ISBN 10: 9819920957 ISBN 13: 9789819920952
Language: English
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£ 150.74
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Machine Learning Assisted Evolutionary Multi and Many Objective Optimization
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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization (Genetic and Evolutionary Computation)
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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization (Hardcover)
Published by Springer Verlag, Singapore, Singapore, 2024
ISBN 10: 9819920957 ISBN 13: 9789819920952
Language: English
Hardcover. Condition: new. Hardcover. This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMaO). EMaO algorithms, namely EMaOAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EMaOAs amenable to application of ML for different pursuits. Recognizing the immense potential for ML-based enhancements in the EMaO 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 EMaO 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 EMaOAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMaOA 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 EMaOA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EMaOA and ML domains. This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMaO). Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMaOA domain.To aid readers, the book includes working codes for the developed algorithms. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization
Published by Springer Nature Singapore, Springer Nature Singapore, 2024
ISBN 10: 9819920957 ISBN 13: 9789819920952
Language: English
£ 156.03
Convert currency£ 12.10 shipping from Germany to United KingdomQuantity: 1 available
Add to basketBuch. Condition: 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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£ 161.88
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Machine Learning Assisted Evolutionary Multi and Many Objective Optimization
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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£ 159.87
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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization (Hardcover)
Published by Springer Verlag, Singapore, Singapore, 2024
ISBN 10: 9819920957 ISBN 13: 9789819920952
Language: English
£ 142.66
Convert currency£ 36.89 shipping from U.S.A. to United KingdomQuantity: 1 available
Add to basketHardcover. Condition: new. Hardcover. This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMaO). EMaO algorithms, namely EMaOAs, iteratively evolve a set of solutions towards a good Pareto Front approximation. The availability of multiple solution sets over successive generations makes EMaOAs amenable to application of ML for different pursuits. Recognizing the immense potential for ML-based enhancements in the EMaO 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 EMaO 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 EMaOAs. Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMaOA 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 EMaOA domain. It serves as an inspiration for further research and applications at the synergistic intersection of EMaOA and ML domains. This book focuses on machine learning (ML) assisted evolutionary multi- and many-objective optimization (EMaO). Finally, this book shares insightful perspectives on the future potential for ML based enhancements in the EMaOA domain.To aid readers, the book includes working codes for the developed algorithms. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization
Published by Springer Nature Singapore, Springer Nature Singapore Mai 2024, 2024
ISBN 10: 9819920957 ISBN 13: 9789819920952
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Add to basketBuch. Condition: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 260 pp. Englisch.
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£ 170.96
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Multi-Objective Machine Learning (Studies in Computational Intelligence, 16)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Multi-Objective Machine Learning (Studies in Computational Intelligence, 16)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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£ 192.12
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Add to basketCondition: New. 2024th edition NO-PA16APR2015-KAP.
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Multi-Objective Machine Learning
Published by Springer Berlin Heidelberg, 2010
ISBN 10: 3642067964 ISBN 13: 9783642067969
Language: English
£ 190.70
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Add to basketTaschenbuch. Condition: 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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Machine Learning Assisted Evolutionary Multi and Many Objective Optimization
Published by Springer-Nature New York Inc, 2024
ISBN 10: 9819920957 ISBN 13: 9789819920952
Language: English
Hardcover. Condition: Brand New. 259 pages. 9.25x6.10x9.21 inches. In Stock.
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Multi-Objective Machine Learning
Published by Springer Berlin Heidelberg, 2006
ISBN 10: 3540306765 ISBN 13: 9783540306764
Language: English
£ 202.95
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Add to basketGebunden. Condition: 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.
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£ 175.55
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£ 175.72
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