Wednesday 5 June 2013

Decision Forests for Computer Vision and Medical Image Analysis

Decision Forests for Computer Vision and Medical Image Analysis
Author:
Edition: 2013
Binding: Kindle Edition
ISBN: B00BLPRYKW



Decision Forests for Computer Vision and Medical Image Analysis (Advances in Computer Vision and Pattern Recognition)


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. Download Decision Forests for Computer Vision and Medical Image Analysis (Advances in Computer Vision and Pattern Recognition) from rapidshare, mediafire, 4shared. 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 Search and find a lot of computer books in many category availabe for free download.

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Decision Forests for Computer Vision and Medical Image Analysis Free


Decision Forests for Computer Vision and Medical Image Analysis computer books for free. Topics and features: with a foreword by Prof. Y. Amit and Prof. D opics 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

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