Multi objective machine learning (53 resultados)
Multi-Objective Decision Making (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Idioma: Inglés
Editorial: Springer 2017
Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 8 de 14. Libro 8 de 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Multi-Objective Decision Making (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Idioma: Inglés
Editorial: Springer 2017
Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning, Libro 8 de 14. Libro 8 de 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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Condición: New. In English.
Machine Learning Assisted Evolutionary Multi and Many Objective Optimization
Saxena, Dhish Kumar; Mittal, Sukrit; Deb, Kalyanmoy; Goodman, Erik D.
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Hardcover. Condición: 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 generati…ons 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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Librería: Romtrade Corp., STERLING HEIGHTS, MI, Estados Unidos de AmericaRomtrade Corp.
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EUR 182,64
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Condición: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Librería: Basi6 International, Irving, TX, Estados Unidos de AmericaBasi6 International
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EUR 182,64
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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization (Genetic and Evolutionary Computation)
Saxena, Dhish Kumar; Mittal, Sukrit; Deb, Kalyanmoy; Goodman, Erik D.
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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Machine Learning Assisted Evolutionary Multi and Many Objective Optimization
Saxena, Dhish Kumar; Mittal, Sukrit; Deb, Kalyanmoy; Goodman, Erik D.
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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Machine Learning Assisted Evolutionary Multi and Many Objective Optimization
Saxena, Dhish Kumar; Mittal, Sukrit; Deb, Kalyanmoy; Goodman, Erik D.
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Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization
Saxena, Dhish Kumar|Mittal, Sukrit|Deb, Kalyanmoy|Goodman, Erik D.
Idioma: Inglés
Editorial: Springer, Berlin|Springer Nature Singapore|Springer 2023
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Condición: Sehr gut. Zustand: Sehr gut | Seiten: 660 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
Machine Learning Assisted Evolutionary Multi and Many Objective Optimization
Saxena, Dhish Kumar; Mittal, Sukrit; Deb, Kalyanmoy; Goodman, Erik D.
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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Taschenbuch. Condición: Neu. Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization | Dhish Kumar Saxena (u. a.) | Taschenbuch | Genetic and Evolutionary Computation | xv | Englisch | 2025 | Springer | EAN 9789819920983 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 He…idelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization (Genetic and Evolutionary Computation)
Saxena, Dhish Kumar; Mittal, Sukrit; Deb, Kalyanmoy; Goodman, Erik D.
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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Condición: New. 2024th edition NO-PA16APR2015-KAP.
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hardcover. Condición: New. In shrink wrap. Looks like an interesting title.
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering.
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Buch. Condición: 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 s…olution 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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Condición: Used. pp. 676.
Machine Learning Assisted Evolutionary Multi- and Many- Objective Optimization
Saxena, Dhish Kumar; Mittal, Sukrit; Deb, Kalyanmoy; Goodman, Erik D.
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Taschenbuch. Condición: Neu. Multi-Objective Machine Learning | Yaochu Jin | Taschenbuch | Studies in Computational Intelligence | 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 | Anbiete…r: preigu.
Machine Learning Assisted Evolutionary Multi and Many Objective Optimization
Saxena, Dhish Kumar/ Mittal, Sukrit/ Deb, Kalyanmoy/ Goodman, Erik D.
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Librería: Revaluation Books, Exeter, , Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 259 pages. 9.25x6.10x9.21 inches. In Stock.
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Condición: Used. pp. 676 Illus.
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Condición: 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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Taschenbuch. Condición: 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-objec…tive 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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Condición: 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 t…he 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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Condición: New. pp. 676.
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EUR 262,78
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Hardcover. Condición: 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 generati…ons 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 Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 225,03
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Buch. Condición: 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 ap…proach 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.
