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Joint learning of error-correcting output codes and dichotomizers from data

Content Provider	Springer Nature Link
Author	Zhong, Guoqiang Huang, Kaizhu Liu, Cheng Lin
Copyright Year	2011
Abstract	The ECOC technique is a powerful tool to learn and combine multiple binary learners for multi-class classification. It generally involves three steps: coding, dichotomizers learning, and decoding. In previous ECOC methods, the coding step and the dichotomizers learning step are usually performed independently. This simplifies the learning problem but may lead to unsatisfactory decoding results. To solve this problem, we propose a novel model for learning the ECOC matrix and dichotomizers jointly from data. We formulate the model as a nonlinear programming problem and develop an efficient alternating minimization algorithm to solve it. Specifically, for fixed ECOC matrix, our model is decomposed into a group of mutually independent quadratic programming problems; while for fixed dichotomizers, it is a difference of convex functions problem and can be easily solved using the concave--convex procedure algorithm. Our experimental results on ten data sets from the UCI machine learning repository demonstrated the advantage of our model over state-of-the-art ECOC methods.
Starting Page	715
Ending Page	724
Page Count	10
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	21
Issue Number	4
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-06-05
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Error correcting output codes (ECOC) Dichotomizer Concave--convex procedure (CCCP) Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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