The nature of statistical learning theory
The nature of statistical learning theory
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
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The Error-Correcting Output Codes (ECOC) method reduces the multi-class learning problem into a series of binary classifiers. In this paper, we propose a modified Hadamard-type ECOC method. This method uses both N'th order and N/2'th-order Hadamard matrix to construct error correcting output codes, which is called Hybrid Hadamard ECOC. Experiments based on dichotomizers of Support Vector Machines (SVM) have been carried out to evaluate the performance of the proposed method. When compared to normal Hadamard ECOC, computation of the method is reduced greatly while the accuracy of classification only drops slightly.