Compact Design of ECOC for Multi-class Object Categorization
نویسندگان
چکیده
In this paper, we propose a Compact design of Error Correcting Output Codes (ECOC) in terms of the number of dichotomizers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best Compact ECOC code configuration. The results over several challenging multi-class Computer Vision problems show comparable and even better results than stateof-the-art ECOC methodologies with far less cost.
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