中文摘要：

多分类问题一直是模式识别领域的一个热点,本文提出了将Hadamard纠错码同二元分类器相结合的方法来解决此问题,相对于其它类型的纠错码多分类器法,该方法的实现简单快捷,且更容易构造出性能优越的纠错码本.本文将Hadamard纠错码和支持向量机相结合,应用于说话人辨认这样一个多分类问题中,并同传统的＂1对余＂的多类推广方式进行了比较.实验结果表明在多分类任务中,Hadamard纠错码对于不同的类别都表现出了很强的分类能力,且性能优于＂1对余＂法,对于类间码字的不同分配方式也具有良好的鲁棒性。

英文摘要：

In this paper, we proposed applying Hadamard Error-Correcting Output Code to extend binary classifier to multiclass problems. Compared with other ECOC approaches, Hadamard ECOC is easy to construct and suitable to any number of classes. We combine it with binary support vector machine （SVM） to solve the multi-class problem of speaker identification. Compared with the traditional ＂1-against-rest＂ method, the experiment result shows that Hadamard ECOC has much better and more stable performance to any number of classes for the multi-class problem and is robust with respect to the assignment of distributed representations to particular classes.