| 引用本文: | 徐图,何大可.超球体多类支持向量机理论[J].控制理论与应用,2009,26(11):1293~1297.[点击复制] |
| Xu Tu,HE Da-ke.Theory of hypersphere multiclass SVM[J].Control Theory and Technology,2009,26(11):1293~1297.[点击复制] |
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| 超球体多类支持向量机理论 |
| Theory of hypersphere multiclass SVM |
| 摘要点击 3146 全文点击 1464 投稿时间：2008-03-17 修订日期：2009-02-24 |
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| DOI编号 10.7641/j.issn.1000-8152.2009.11.CCTA080196 |
| 2009,26(11):1293-1297 |
| 中文关键词 支持向量机 多类支持向量机 SMO训练算法 推广性能 超球体多类支持向量机 |
| 英文关键词 support vector machine(SVM) multi-class SVM SMO algorithm generalization performance HSMC-SVM |
| 基金项目 西南交通大学青年教师科研起步项目(2008Q109). |
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| 中文摘要 |
| 目前的多类分类器大多是经二分类器组合而成的, 存在训练速度较慢的问题, 在分类类别多的时候, 会遇到很大困难, 超球体多类支持向量机将超球体单类支持向量机扩展到多类问题, 由于每类样本只参与一个超球体支持向量机的训练, 因此, 这是一种直接多类分类器, 训练效率明显提高. 为了有效训练超球体多类支持向量机, 利用SMO算法思想, 提出了超球体支持向量机的快速训练算法. 同时对超球体多类支持向量机的推广能力进行了理
论上的估计. 数值实验表明, 在分类类别较多的情况, 这种分类器的训练速度有很大提高, 非常适合解决类别数较多的分类问题. 超球体多类支持向量机为研究快速直接多类分类器提供了新的思路. |
| 英文摘要 |
| Constructed by standard binary classes support vector machine(SVM), present multiclass SVMs are usually very slow to be trained. When a large number of categories of data are to be classified, the training work could be very
difficult. By extending the hypersphere one-class SVM(HSOC-SVM) to a hypersphere multiclass SVM(HSMC-SVM), we build a fast training classifier HSOC-SVM. Its training speed is higher than that of the present multiclass classifiers, because each category data trains only one HSOC-SVM. In order to improve the training speed for the HSMC-SVM, we propose a training algorithm based on the existing algorithm for SMO. Meanwhile, the theoretic upper bound of the generalized error of HSMC-SVM is analyzed for evaluating the general performance of HSMC-SVM. Numeric experiments show that the training speed of HSMC-SVM is especially improved when many categories of data are to be classified. Thus, HSMC-SVM provides a new idea for developing fast-directed multiclass classifiers in machine learning area. |
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