@inproceedings{fa9af3e98bf541a8a6f0c432192125cf,
title = "Totally-corrective multi-class boosting",
abstract = "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.",
author = "Zhihui Hao and Chunhua Shen and Nick Barnes and Bo Wang",
year = "2011",
doi = "10.1007/978-3-642-19282-1\_22",
language = "English",
isbn = "9783642192814",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 4",
pages = "269--280",
booktitle = "Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers",
address = "Germany",
edition = "PART 4",
}