| 支持向量机多类分类方法 |
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| 引用本文： | 苟博,黄贤武.支持向量机多类分类方法[J].数据采集与处理,2006,21(3):334-339. |
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| 作者姓名： | 苟博 黄贤武 |
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| 作者单位： | 苏州大学电子信息学院,苏州,215021 |
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| 摘 要： | 支持向量机本身是一个两类问题的判别方法，不能直接应用于多类问题。当前针对多类问题的支持向量机分类方法主要有5种：一类对余类法（OVR），一对一法（OVO），二叉树法（BT），纠错输出编码法和有向非循环图法。本文对这些方法进行了简单的介绍，通过对其原理和实现方法的分析，从速度和精度两方面对这些方法的优缺点进行了归纳和总结，给出了比较意见，并通过实验进行了验证，最后提出了一些改进建议。
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| 关 键 词： | 支持向量机 序列最小最优化算法 多类分类 多类支持向量机 |
| 文章编号： | 1004-9037（2006）03-0334-06 |
| 收稿时间： | 2005-08-18 |
| 修稿时间： | 2005-08-182006-02-02 |
SVM Multi-Class Classification |
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| Gou Bo,Huang Xianwu.SVM Multi-Class Classification[J].Journal of Data Acquisition & Processing,2006,21(3):334-339. |
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| Authors: | Gou Bo Huang Xianwu |
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| Affiliation: | School of Electronics and Information Engineering, SooChow University, Suzhou, 215021, China |
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| Abstract: | The support vector machine(SVM) is used for the binary-class classification.It cannot deal with multi-class classification directly.Five methods for multi-class classification are introduced based on widely used SVMs.They are one versus rest(OVR),one versus one(OVO),binary tree(BT),error correcting output codes(ECOC) and directed acyclic graph(DAG).A comparison result about the classification speed and accuracy is given through theoretic analysis.Experimental results demonstrate the comparison result.In addition,some suggestions for improving these methods are also presented. |
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| Keywords: | support vector machine sequential minimal optimization multi-class classification multi-class support vector machine |
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