Synopsis
This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests ina hands-on manner.
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Decision Forests for Computer Vision and Medical Image Analysis (Advances in Computer Vision and Pattern Recognition)
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Decision Forests for Computer Vision and Medical Image Analysis
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces a flexible decision forest model capable of addressing a large and diverse set of image and video analysis tasks, covering both theoretical foundations and practical implementationIncludes exercises and experiments throughout the text, . N° de réf. du vendeur 447761646
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Decision Forests for Computer Vision and Medical Image Analysis
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Springer London Aug 2016, 2016
ISBN 10 : 144716962X
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests ina hands-on manner. 392 pp. Englisch. N° de réf. du vendeur 9781447169628
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Taschenbuch. Etat : Neu. Decision Forests for Computer Vision and Medical Image Analysis | J. Shotton (u. a.) | Taschenbuch | xix | Englisch | 2016 | Springer London | EAN 9781447169628 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. N° de réf. du vendeur 103426183
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Decision Forests for Computer Vision and Medical Image Analysis (Advances in Computer Vision and Pattern Recognition)
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Decision Forests for Computer Vision and Medical Image Analysis
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Springer London, Springer London Aug 2016, 2016
ISBN 10 : 144716962X
ISBN 13 : 9781447169628
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Taschenbuch. Etat : Neu. Neuware -This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests ina hands-on manner.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 392 pp. Englisch. N° de réf. du vendeur 9781447169628
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