9789819626861 - Computational Intelligence for High-Dimensional Machine Learning: A Feature Selection Perspective and Its Real-World Applications (SpringerBriefs in Computer Scienc... (9 resultados)

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book focuses on themodelling and optimization aspects of the feature selection problem throughcomputational intelligence methods in complex, high-dimensional supervisedmachine learning. To aidreaders in conductingresearch in this field, it covers fundamental concepts and state-of-the-artalgorithms. This book also providesa detailed insight into applying these algorithms into real-world applications.The authors begin by introducing thedefinition high-dimensional machine learning (ML) problems and the challenges they pose. Subsequently, they delve intodimension reduction methods for high-dimensional ML, including global and localfeature selection (FS) techniques. This book also comprehensively presents computationalintelligence methods such as evolutionary computation and deep neural networksfor FS, supported by boththeoretical and empirical evidence. Furthermore, this book explores real-worldscenario applications involving high-dimensional ML, particularly in the context of smartcities, bioinformatics and industrial informatics.This book is a suitable readfor postgraduates and researchers who are interested in the research areas ofcomputational intelligence, soft computing, machine learning and deep learning.Professionals and practitioners within these related fields will also benefitfrom this book.

Taschenbuch. Condición: Neu. Computational Intelligence for High-Dimensional Machine Learning | A Feature Selection Perspective and Its Real-World Applications | Yu Zhou (u. a.) | Taschenbuch | ix | Englisch | 2025 | Springer | EAN 9789819626861 | 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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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book focuses on themodelling and optimization aspects of the feature selection problem throughcomputational intelligence methods in complex, high-dimensional supervisedmachine learning. To aidreaders in conductingresearch in this field, it covers fundamental concepts and state-of-the-artalgorithms. This book also providesa detailed insight into applying these algorithms into real-world applications.The authors begin by introducing thedefinition high-dimensional machine learning (ML) problems and the challenges they pose. Subsequently, they delve intodimension reduction methods for high-dimensional ML, including global and localfeature selection (FS) techniques. This book also comprehensively presents computationalintelligence methods such as evolutionary computation and deep neural networksfor FS, supported by boththeoretical and empirical evidence. Furthermore, this book explores real-worldscenario applications involving high-dimensional ML, particularly in the context of smartcities, bioinformatics and industrial informatics.This book is a suitable readfor postgraduates and researchers who are interested in the research areas ofcomputational intelligence, soft computing, machine learning and deep learning.Professionals and practitioners within these related fields will also benefitfrom this book. 132 pp. Englisch.

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book focuses on the modelling and optimization aspects of the feature selection problem through computational intelligence methods in complex, high-dimensional supervised machine learning. To aid readers in conducting research in this field, it covers fundamental concepts and state-of-the-art algorithms. This book also provides a detailed insight into applying these algorithms into real-world applications. The authors begin by introducing the definition high-dimensional machine learning (ML) problems and the challenges they pose. Subsequently, they delve into dimension reduction methods for high-dimensional ML, including global and local feature selection (FS) techniques. This book also comprehensively presents computational intelligence methods such as evolutionary computation and deep neural networks for FS, supported by both theoretical and empirical evidence. Furthermore, this book explores real-world scenario applications involving high-dimensional ML, particularly in the context of smart cities, bioinformatics and industrial informatics.This book is a suitable read for postgraduates and researchers who are interested in the research areas of computational intelligence, soft computing, machine learning and deep learning. Professionals and practitioners within these related fields will also benefit from this book.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch.