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
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Multi-Classification by Using Tri-Class SVM
Neural Processing Letters
Dual unification of bi-class support vector machine formulations
Pattern Recognition
Support vector machines for classification of input vectors with different metrics
Computers & Mathematics with Applications
Two Criteria for Model Selection in Multiclass Support Vector Machines
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A Note on the Bias in SVMs for Multiclassification
IEEE Transactions on Neural Networks
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Applied Soft Computing
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The use of binary support vector machines (SVMs) in multi-classification is addressed in this paper. Margins associated to the bi-classifiers, since they depend on the geometrical disposition of the classes being separated, are, in general, of various magnitudes. In order to overcome this scaling problem, a normalization process should be applied on the SVMs' outputs. Thus, a new normalization approach is presented based on the convex hulls that contain the classes to be separated. Furthermore, a theoretical study is developed which justifies the proposed approach, and an interpretation is provided. An empirical study is also carried out to compare this normalization with others found in the literature.