NDLI: Robust and efficient regularized boosting using total Bregman divergence

Content Provider	IEEE Xplore Digital Library
Author	Meizhu Liu Vemuri, B.C.
Copyright Year	2011
Description	Author affiliation: CISE, University of Florida (Meizhu Liu; Vemuri, B.C.)
Abstract	Boosting is a well known machine learning technique used to improve the performance of weak learners and has been successfully applied to computer vision, medical image analysis, computational biology and other fields. A critical step in boosting algorithms involves update of the data sample distribution, however, most existing boosting algorithms use updating mechanisms that lead to overfitting and instabilities during evolution of the distribution which in turn results in classification inaccuracies. Regularized boosting has been proposed in literature as a means to overcome these difficulties. In this paper, we propose a novel total Bregman divergence (tBD) regularized LPBoost, termed tBRLPBoost. tBD is a recently proposed divergence in literature, which is statistically robust and we prove that tBRLPBoost requires a constant number of iterations to learn a strong classifier and hence is computationally more efficient compared to other regularized boosting algorithms in literature. Also, unlike other boosting methods that are only effective on a handful of datasets, tBRLPBoost works well on a variety of datasets. We present results of testing our algorithm on many public domain databases along with comparisons to several other state-of-the-art methods. Numerical results depict much improvement in efficiency and accuracy over competing methods.
Starting Page	2897
Ending Page	2902
File Size	207285
Page Count	6
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995686
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Boosting Training Robustness Noise measurement Testing Databases
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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