Skip navigation
Skip navigation

Laplacian Margin Distribution Boosting for Learning from Sparsely Labeled Data

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


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
Source: A Novel Illumination-Invariant Loss for Monocular 3D Pose Estimation
DOI: 10.1109/DICTA.2011.42


File Description SizeFormat Image
01_Wang_Laplacian_Margin_Distribution_2011.pdf553.33 kBAdobe PDF    Request a copy

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  12 November 2018/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator