Error Correcting Output Codes vs. Fuzzy Support Vector Machines
نویسندگان
چکیده
Error correcting output codes (ECOC) have been proposed to enhance generalization ability of classifiers. If, instead of discrete error functions, continuous error functions are used, unclassifiable regions of multiclass support vector machines are resolved. In this paper, we discuss minimum operations as well as average operations for error functions of support vector machines and show the equivalence of ECOC support vector machines and fuzzy support vector machines for one-against-all formulation. Then we show by computer simulations that ECOC support vector machines are not always superior to one-against-all fuzzy support vector machines.
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