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Type: Conference paper
Title: A direct formulation for totally-corrective multi-class boosting
Author: Shen, C.
Hao, Z.
Citation: IEEE CVPR 2011 Conference Colorado Springs: Computer Vision and Pattern Recognition (CVPR) 2011, June 21-23, 2011, pp. 2585-2592
Publisher: IEEE
Publisher Place: USA
Issue Date: 2011
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781457703942
ISSN: 1063-6919
Conference Name: IEEE Conference on Computer Vision and Pattern Recognition (24th : 2011 : Colorado Springs, CO, U.S.A.)
Statement of
Chunhua Shen and Zhihui Hao
Abstract: Boosting combines a set of moderately accurate weak classifiers to form a highly accurate predictor. Compared with binary boosting classification, multi-class boosting received less attention. We propose a novel multi-class boosting formulation here. Unlike most previous multi-class boosting algorithms which decompose a multi-boost problem into multiple independent binary boosting problems, we formulate a direct optimization method for training multi-class boosting. Moreover, by explicitly deriving the Lagrange dual of the formulated primal optimization problem, we design totally-corrective boosting using the column generation technique in convex optimization. At each iteration, all weak classifiers’ weights are updated. Our experiments on various data sets demonstrate that our direct multi-class boosting achieves competitive test accuracy compared with state-of-the-art multi-class boosting in the literature.
Keywords: Boosting, multi-class classification
Rights: © 2011 IEEE
DOI: 10.1109/CVPR.2011.5995554
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