...透过 SVM 分 类器只可将资料分成两个类别（是或者不是），如果将 SVM 运用在分类 多个类别的分类问题时（multi-class problems），将要加以改变支援向量的 方法。
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The method constructing and combining several binary SVMs(Support Vector Machines) with a binary tree can solve multiclass problems,and resolve the unclassifiable regions that exist in the conventional multiclass SVMs.
采用二叉树结构对多个二值支持向量机(SVM)子分类器组合，可实现多类问题的分类，并且还可克服传统多类SVM算法存在的不可分区域的情况。
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The presented multi-class SVM is of better classification ability and can solve the unclassifiable region problems int.
新的多类SVM在一定程度上解决了传统投票决策方法的不可分区域问题，因此具有更好的分类性能。
The problems and defections of the existing methods of SVM multi-class classification were analyzed. A multi-class classification based on binary tree was put forward.
介绍了几种常用的支持向量机多类分类方法，分析其存在的问题及缺点。
Multi-attribute decision making of interval numbers is one of a class of uncertain multi-attribute decision making problems.
区间数多属性决策问题就是其中一类不确定多属性决策问题。
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