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Type: Conference paper
Title: Fast training of effective multi-class boosting using coordinate descent optimization
Author: Lin, G.
Shen, C.
Van Den Hengel, A.
Suter, D.
Citation: Computer Vision - ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, revised selected papers, part II / K.M. Lee, Y. Matsushita, J.M. Rehg and Z. Hu (eds.): pp.782-795
Publisher: Springer-Verlag
Publisher Place: Germany
Issue Date: 2012
Series/Report no.: Lecture Notes in Computer Science; 7725
ISBN: 9783642374432
ISSN: 0302-9743
Conference Name: Asian Conference on Computer Vision (11th : 2012 : Daejeon, Korea)
Statement of
Guosheng Lin, Chunhua Shen, Anton van den Hengel and David Suter
Abstract: We present a novel column generation based boosting method for multi-class classification. Our multi-class boosting is formulated in a single optimization problem as in [1]. Different from most existing multiclass boosting methods, which use the same set of weak learners for all the classes, we train class specified weak learners (i.e., each class has a different set of weak learners). We show that using separate weak learner sets for each class leads to fast convergence, without introducing additional computational overhead in the training procedure. To further make the training more efficient and scalable, we also propose a fast coordinate descent method for solving the optimization problem at each boosting iteration. The proposed coordinate descent method is conceptually simple and easy to implement in that it is a closed-form solution for each coordinate update. Experimental results on a variety of datasets show that, compared to a range of existing multi-class boosting methods, the proposed method has much faster convergence rate and better generalization performance in most cases. We also empirically show that the proposed fast coordinate descent algorithm needs less training time than the MultiBoost algorithm in [1].
Keywords: Image processing and computer vision
pattern recognition
artificial intelligence
health informatics
Rights: © Springer-Verlag Berlin Heidelberg 2013
DOI: 10.1007/978-3-642-37444-9_61
Appears in Collections:Aurora harvest
Computer Science publications

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