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