Skip navigation
Skip navigation

Totally-Corrective Multi-class Boosting

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

Description

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
URI: http://hdl.handle.net/1885/62216
Source: Proceedings of ACCV 2010
DOI: 10.1007/978-3-642-19282-1_22

Download

File Description SizeFormat Image
01_Hao_Totally-Corrective_Multi-class_2010.pdf246.03 kBAdobe PDF    Request a copy


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

Updated:  23 August 2018/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator