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首页> 外文期刊>IEEE Transactions on Neural Networks >Binary tree of SVM: a new fast multiclass training and classification algorithm
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Binary tree of SVM: a new fast multiclass training and classification algorithm

机译：支持向量机的二叉树：一种新的快速多类训练和分类算法

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摘要

We present a new architecture named Binary Tree of support vector machine (SVM), or BTS, in order to achieve high classification efficiency for multiclass problems. BTS and its enhanced version, c-BTS, decrease the number of binary classifiers to the greatest extent without increasing the complexity of the original problem. In the training phase, BTS has N-1 binary classifiers in the best situation (N is the number of classes), while it has log/sub 4/3/((N+3)/4) binary tests on average when making a decision. At the same time the upper bound of convergence complexity is determined. The experiments in this paper indicate that maintaining comparable accuracy, BTS is much faster to be trained than other methods. Especially in classification, due to its Log complexity, it is much faster than directed acyclic graph SVM (DAGSVM) and ECOC in problems that have big class number.
机译：我们提出了一种名为支持向量机（SVM）或BTS的二叉树的新架构，以实现针对多类问题的高分类效率。 BTS及其增强版本c-BTS在不增加原始问题复杂性的情况下，最大程度地减少了二进制分类器的数量。在训练阶段，BTS在最佳情况下具有N-1个二进制分类器（N是班级数量），而在进行训练时，BTS平均具有log / sub 4/3 /（（（N + 3）/ 4）个二进制测试决定。同时确定收敛复杂度的上限。本文中的实验表明，BTS保持可比的准确性，比其他方法训练起来要快得多。特别是在分类中，由于其Log复杂性，它在类数较大的问题中比有向无环图SVM（DAGSVM）和ECOC快得多。

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