Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Approximate minimum enclosing balls in high dimensions using core-sets
Journal of Experimental Algorithmics (JEA)
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Multiclass Classification with Multi-Prototype Support Vector Machines
The Journal of Machine Learning Research
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Fast Single-Shot Multiclass Proximal Support Vector Machines and Perceptions
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Discriminatively regularized least-squares classification
Pattern Recognition
Fast support vector machines for continuous data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
From minimum enclosing ball to fast fuzzy inference system training on large datasets
IEEE Transactions on Fuzzy Systems
Ordinal-class core vector machine
Journal of Computer Science and Technology
Rough margin based core vector machine
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Reduced universal background model for speech recognition and identification system
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
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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.