Abstract
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.
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Zhong, G., Huang, K., Liu, CL. (2010). Learning ECOC and Dichotomizers Jointly from Data. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_61
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DOI: https://doi.org/10.1007/978-3-642-17537-4_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17536-7
Online ISBN: 978-3-642-17537-4
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