Using Hadamard ECOC in multi-class problems based on SVM

In this paper, we propose to apply Hadamard Error-Correcting Output Code (Hadamard ECOC) to extend binary classifier for multi-class classification problems. Hadamard ECOC is easy to construct and is suitable for any number of classes. We combine it with binary support vector machine (SVM) to solve the multi-class problem of speaker identification, which takes advantage of error correcting ability of Hadamard ECOC and powerful classification ability of SVM. Compared to the traditional "1-against-rest" method, the experiment result shows that Hadamard ECOC approach has much better and more stable performance for the multiclass problem and is robust on different rules mapping rules between ECOCs and classes.