Multiclass core vector machine
Multiclass core vector machine
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DOI：
10.1145/1273496.1273502
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发表时间：
2007-06
期刊：
影响因子：
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通讯作者：
S. Asharaf;M. Murty;S. Shevade
中科院分区：
文献类型：
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作者：
S. Asharaf;M. Murty;S. Shevade
尽管文献中提出了几种使用支持向量机（SVM）实现多类分类的技术，但这些方法处理大型数据集的可扩展性方面仍需要进行大量探索。核心向量机（CVM）是一种将两类支持向量机扩展到处理大型数据集的技术。本文提出了一种多类核向量机（MCVM）。在这里，我们将多类支持向量机问题表述为一个二次规划（QP）问题，该问题定义了一个具有向量值输出的支持向量机。然后使用CVM技术解决这个QP问题，以实现处理大型数据集的可伸缩性。在几个大型合成数据集和真实世界数据集上进行的实验表明，所提出的MCVM技术在计算开销少得多的情况下具有与SVM相同的良好泛化性能。此外，可以观察到MCVM可以很好地随数据集的大小进行扩展。
Even though several techniques have been proposed in the literature for achieving multiclass classification using Support Vector Machine(SVM), the scalability aspect of these approaches to handle large data sets still needs much of exploration. Core Vector Machine(CVM) is a technique for scaling up a two class SVM to handle large data sets. In this paper we propose a Multiclass Core Vector Machine(MCVM). Here we formulate the multiclass SVM problem as a Quadratic Programming(QP) problem defining an SVM with vector valued output. This QP problem is then solved using the CVM technique to achieve scalability to handle large data sets. Experiments done with several large synthetic and real world data sets show that the proposed MCVM technique gives good generalization performance as that of SVM at a much lesser computational expense. Further, it is observed that MCVM scales well with the size of the data set.