TY - GEN
T1 - Totally-corrective multi-class boosting
AU - Hao, Zhihui
AU - Shen, Chunhua
AU - Barnes, Nick
AU - Wang, Bo
PY - 2011
Y1 - 2011
N2 - 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 datasets show that the new algorithms have comparable generalization capability but converge much faster than their counterparts. Experiments on MNIST handwriting digit classification also demonstrate the effectiveness of the proposed algorithms.
AB - 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 datasets show that the new algorithms have comparable generalization capability but converge much faster than their counterparts. Experiments on MNIST handwriting digit classification also demonstrate the effectiveness of the proposed algorithms.
UR - http://www.scopus.com/inward/record.url?scp=79952532175&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-19282-1_22
DO - 10.1007/978-3-642-19282-1_22
M3 - Conference contribution
AN - SCOPUS:79952532175
SN - 9783642192814
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 269
EP - 280
BT - Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
T2 - 10th Asian Conference on Computer Vision, ACCV 2010
Y2 - 8 November 2010 through 12 November 2010
ER -