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Totally-Corrective Multi-class Boosting

Hao, Zhihui; Shen, Chunhua; Barnes, Nick; Wang, Bo


We proffer totally-corrective multi-class boosting algorithms in this work. First, we discuss the methods that extend two-class boosting to multi-class case by studying two existing boosting algorithms: AdaBoost.MO and SAMME, and formulate convex optimization problems that minimize their regularized cost functions. Then we propose a column-generation based totally-corrective framework for multi-class boosting learning by looking at the Lagrange dual problems. Experimental results on UCI...[Show more]

CollectionsANU Research Publications
Date published: 2010
Type: Conference paper
Source: Proceedings of ACCV 2010
DOI: 10.1007/978-3-642-19282-1_22


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