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Laplacian Margin Distribution Boosting for Learning from Sparsely Labeled Data

Wang, Tao; He, Xuming; Shen, Chunhua; Barnes, Nick

Description

Boosting algorithms attract much attention in computer vision and image processing because of their strong performance in a variety of applications. Recent progress on the theory of boosting algorithms suggests a close link between good generalization and the margin distrubtion of the classifier \wrt a dataset. In this paper, we propose a novel data-dependent margin distribution learning criterion for boosting, termed Laplacian MDBoost, which utilizes the intrinsic geometric structure of...[Show more]

CollectionsANU Research Publications
Date published: 2011
Type: Conference paper
URI: http://hdl.handle.net/1885/50628
Source: A Novel Illumination-Invariant Loss for Monocular 3D Pose Estimation
DOI: 10.1109/DICTA.2011.42

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