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1st ed. 18 x 25 cm. 480 pages. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
Anbieter: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Deutschland
xiv, 208 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
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In den WarenkorbZustand: New. pp. 500 68:B&W 7 x 10 in or 254 x 178 mm Case Laminate on White w/Gloss Lam.
Anbieter: Kennys Bookstore, Olney, MD, USA
Zustand: New.
Sprache: Englisch
Verlag: Engineering Science Reference, 2019
ISBN 10: 1799801829 ISBN 13: 9781799801825
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Hardcover. Zustand: Very Good.
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In den WarenkorbPaperback. Zustand: Brand New. 370 pages. 11.69x8.25x11.69 inches. In Stock.
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In den WarenkorbZustand: Sehr gut. Zustand: Sehr gut | Seiten: 500 | Sprache: Englisch | Produktart: Bücher | Machine learning is the study of building computer programs that improve their performance through experience. To meet the challenge of developing and maintaining larger and complex software systems in a dynamic and changing environment, machine learning methods have been playing an increasingly important role in many software development and maintenance tasks. Advances in Machine Learning Applications in Software Engineering provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. This book depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality. Advances in Machine Learning Applications in Software Engineering offers readers suggestions by proposing future work and areas in this emerging research field.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 146,60
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In den WarenkorbZustand: New.
Sprache: Englisch
Verlag: Information Science Reference, 2019
ISBN 10: 1522581006 ISBN 13: 9781522581000
Anbieter: Books From California, Simi Valley, CA, USA
hardcover. Zustand: Very Good.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 160,56
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In den WarenkorbZustand: New.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 166,92
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In den WarenkorbZustand: New.
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
EUR 179,63
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In den WarenkorbZustand: New.
Sprache: Englisch
Verlag: Springer Nature Singapore, 2023
ISBN 10: 981995973X ISBN 13: 9789819959730
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In den WarenkorbZustand: Hervorragend. Zustand: Hervorragend | Seiten: 816 | Sprache: Englisch | Produktart: Bücher | This volume comprises the select peer-reviewed proceedings of the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning 2022 (ICAAAIML 2022). It aims to provide a comprehensive and broad-spectrum picture of state-of-the-art research and development in the areas of artificial intelligence, machine learning, deep learning, and their advanced applications in computer vision and blockchain. It also covers research in core concepts of computers, intelligent system design and deployment, real-time systems, WSN, sensors and sensor nodes, software engineering, image processing, and cloud computing. This volume will provide a valuable resource for those in academia and industry.
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EUR 186,30
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In den WarenkorbZustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data. In recent years, UDA has received significant attention from the research community due to its applicability in various real-world scenarios. This book provides a comprehensive review of state-of-the-art UDA methods and explores new variants of UDA that have the potential to advance the field.The book begins with a clear introduction to the UDA problem and is mainly organized into four technical sections, each focused on a specific piece of UDA research. The first section covers criterion optimization-based UDA, which aims to learn domain-invariant representations by minimizing the discrepancy between source and target domains. The second section discusses bi-classifier adversarial learning-based UDA, which creatively leverages adversarial learning by conducting a minimax game between the feature extractor and two task classifiers. The third section introduces source-free UDA, a novel UDA setting that does not require any raw data from the source domain. The fourth section presents active learning for UDA, which combines domain adaptation and active learning to reduce the amount of labeled data needed for adaptation.This book is suitable for researchers, graduate students, and practitioners who are interested in UDA and its applications in various fields, primarily in computer vision. The chapters are authored by leading experts in the field and provide a comprehensive and in-depth analysis of the current UDA methods and new directions for future research. With its broad coverage and cutting-edge research, this book is a valuable resource for anyone looking to advance their knowledge of UDA.
Sprache: Englisch
Verlag: Springer Nature Switzerland, 2018
ISBN 10: 3319831909 ISBN 13: 9783319831909
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Algorithmic Advances in Riemannian Geometry and Applications | For Machine Learning, Computer Vision, Statistics, and Optimization | Vittorio Murino (u. a.) | Taschenbuch | xiv | Englisch | 2018 | Springer Nature Switzerland | EAN 9783319831909 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, 2018
ISBN 10: 3319831909 ISBN 13: 9783319831909
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.
Sprache: Englisch
Verlag: Springer International Publishing, 2016
ISBN 10: 3319450255 ISBN 13: 9783319450254
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.