Asharaf, S and Narasimha Murty, M and Shevade, SK (2007) Multiclass Core Vector Machine. In: International Conference on Machine Learning (ICML-07), 2007, June 2007, New York, NY.
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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.
|Item Type:||Conference Paper|
|Additional Information:||Copyright of this article belongs to ACM Press.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||17 Oct 2011 08:15|
|Last Modified:||17 Oct 2011 08:15|
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