Applied multivariate statistical analysis
Applied multivariate statistical analysis
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Knowledge-Based multiclass support vector machines applied to vertical two-phase flow
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Object-space multiphase implicit functions
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
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This paper presents a linear programming formulation for linear and nonlinear piecewise multi-classification support vector machines model for multi-category discrimination of sets or objects. The proposed model can be used to generate linear and nonlinear piecewise classifiers depending on the kernel function employed. Advantages of the linear programming multiclassification SVM formulation include its ability to express a multi-class problem as a single optimization problem and its computational tractability in providing the opt imal classification weights for multi-categorical separation. Computational results are provided for validation of the proposed piecewise multi-classification SVM model using four benchmark data sets (GPA, IRIS, WINE, and GLASS data).