9783319831909 - algorithmic advances in riemannian geometry and applications: for machine learning, computer vision, statistics, and optimization (11 risultati)
Lingua: Inglese
Editore: Springer, 2018
Serie: Libro 57 di 86 - Advances in Computer Vision and Pattern Recognition
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Da: Basi6 International, Irving, TX, U.S.A.Basi6 International
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Lingua: Inglese
Editore: Springer, 2018
Serie: Libro 57 di 86 - Advances in Computer Vision and Pattern Recognition
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Taschenbuch. Condizione: Neu. Algorithmic Advances in Riemannian Geometry and Applications | For Machine Learning, Computer Vision, Statistics, and Optimization | Hà Quang Minh (u. a.) | Taschenbuch | Advances in Computer Vision and Pattern Recognition | xiv | Englisch | 2018 | Springer | EAN 9783319831909 | Verantwortliche Pers…on für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer, 2018
Serie: Libro 57 di 86 - Advances in Computer Vision and Pattern Recognition
- Brossura
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: 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 bo…ok 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.
Lingua: Inglese
Editore: Springer, 2018
Serie: Libro 57 di 86 - Advances in Computer Vision and Pattern Recognition
- Brossura
Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: New. pp. 222.
Lingua: Inglese
Editore: Springer, 2018
Serie: Libro 57 di 86 - Advances in Computer Vision and Pattern Recognition
- Brossura
Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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Paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Lingua: Inglese
Editore: Springer, 2018
Serie: Libro 57 di 86 - Advances in Computer Vision and Pattern Recognition
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Jun 2018, 2018
Serie: Libro 57 di 86 - Advances in Computer Vision and Pattern Recognition
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: 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 ch…apters 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.
Lingua: Inglese
Editore: Springer International Publishing, 2018
Serie: Libro 57 di 86 - Advances in Computer Vision and Pattern Recognition
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Da: moluna, Greven, Germaniamoluna
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Condizione: 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.
Lingua: Inglese
Editore: Springer International Publishing, Springer Nature Switzerland Jun 2018, 2018
Serie: Libro 57 di 86 - Advances in Computer Vision and Pattern Recognition
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 149,79
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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.
Lingua: Inglese
Editore: Springer, 2018
Serie: Libro 57 di 86 - Advances in Computer Vision and Pattern Recognition
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- Print on Demand
Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Condizione: New. Print on Demand pp. 222.
Lingua: Inglese
Editore: Springer, 2018
Serie: Libro 57 di 86 - Advances in Computer Vision and Pattern Recognition
- Brossura
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 264,85
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Condizione: New. PRINT ON DEMAND pp. 222.
