algorithmic advances riemannian geometry (19 Ergebnisse)

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.

Zustand: New.

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.

Taschenbuch. Zustand: Neu. Neuware -This book presents a selection of the most recent algorithmic advances in Riemanniangeometry in the context of machine learning, statistics, optimization, computervision, and related fields. The unifying theme of the different chapters in the bookis the exploitation of the geometry of data using the mathematical machinery ofRiemannian geometry. As demonstrated by all the chapters in the book, when the datais intrinsically non-Euclidean, the utilization of this geometrical information can leadto better algorithms that can capture more accurately the structures inherent in thedata, leading ultimately to better empirical performance. This book is not intended tobe an encyclopedic compilation of the applications of Riemannian geometry. Instead, itfocuses on several important research directions that are currently actively pursued byresearchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionarylearning and sparse coding on manifolds. Examples of applications include novel algorithmsfor Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 224 pp. Englisch.

Buch. Zustand: Neu. Neuware -This book presents a selection of the most recent algorithmic advances in Riemanniangeometry in the context of machine learning, statistics, optimization, computervision, and related fields. The unifying theme of the different chapters in the bookis the exploitation of the geometry of data using the mathematical machinery ofRiemannian geometry. As demonstrated by all the chapters in the book, when the datais intrinsically non-Euclidean, the utilization of this geometrical information can leadto better algorithms that can capture more accurately the structures inherent in thedata, leading ultimately to better empirical performance. This book is not intended tobe an encyclopedic compilation of the applications of Riemannian geometry. Instead, itfocuses on several important research directions that are currently actively pursued byresearchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionarylearning and sparse coding on manifolds. Examples of applications include novel algorithmsfor Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 224 pp. Englisch.

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.

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.

Zustand: New. pp. 222.

Hardcover. Zustand: Like New. Like New. book.

Paperback. Zustand: New. New. book.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 224 pp. Englisch.

Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 224 pp. Englisch.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Showcases Riemannian geometry as a foundational mathematical framework for solving many problems in machine learning, statistics, optimization, computer vision, and related fields Describes comprehensively the state-of-the-art theory and algorith.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Showcases Riemannian geometry as a foundational mathematical framework for solving many problems in machine learning, statistics, optimization, computer vision, and related fields Describes comprehensively the state-of-the-art theory and algorith.

Buch. Zustand: Neu. Algorithmic Advances in Riemannian Geometry and Applications | For Machine Learning, Computer Vision, Statistics, and Optimization | Vittorio Murino (u. a.) | Buch | xiv | Englisch | 2016 | Springer International Publishing | EAN 9783319450254 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Zustand: New. Print on Demand.

Zustand: New. PRINT ON DEMAND.

Zustand: New. Print on Demand pp. 222.

Zustand: New. PRINT ON DEMAND pp. 222.