The nature of statistical learning theory
The nature of statistical learning theory
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A novel and quick SVM-based multi-class classifier
Pattern Recognition
Multi-classification with tri-class support vector machines: a review
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Three-dimensional imaging information acquisition system based on DSP and SVM
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Support vector machines for classification of input vectors with different metrics
Computers & Mathematics with Applications
Simple solvers for large quadratic programming tasks
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Applied Soft Computing
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SVM theory was originally developed on the basis of a separable binary classification problem, and other approaches have been later introduced. In this paper, we demonstrated that all these approaches admit the same dual problem formulation.