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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Learning ECOC and Dichotomizers Jointly from Data
verfasst von : Guoqiang Zhong, Kaizhu Huang, Cheng-Lin Liu
Erschienen in: Neural Information Processing. Theory and Algorithms
Verlag: Springer Berlin Heidelberg
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In this paper, we present a first study which learns the ECOC matrix as well as dichotomizers simultaneously from data; these two steps are usually conducted independently in previous methods. We formulate our learning model as a sequence of concave-convex programming problems and develop an efficient alternative minimization algorithm to solve it. Extensive experiments over eight real data sets and one image analysis problem demonstrate the advantage of our model over other state-of-the-art ECOC methods in multi-class classification.