Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
Consistency of support vector machines and other regularized kernel classifiers
IEEE Transactions on Information Theory
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
Robust boosting algorithm against mislabeling in multiclass problems
Neural Computation
VC Theory of Large Margin Multi-Category Classifiers
The Journal of Machine Learning Research
ABC-boost: adaptive base class boost for multi-class classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A framework for kernel-based multi-category classification
Journal of Artificial Intelligence Research
Semisupervised multicategory classification with imperfect model
IEEE Transactions on Neural Networks
Multiclass support vector machines for adaptation in MIMO-OFDM wireless systems
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Unsupervised Supervised Learning II: Margin-Based Classification Without Labels
The Journal of Machine Learning Research
Inhibition in multiclass classification
Neural Computation
A generic model of multi-class support vector machine
International Journal of Intelligent Information and Database Systems
A theory of multiclass boosting
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Multicategory large-margin unified machines
The Journal of Machine Learning Research
Information Sciences: an International Journal
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Binary classification is a well studied special case of the classification problem. Statistical properties of binary classifiers, such as consistency, have been investigated in a variety of settings. Binary classification methods can be generalized in many ways to handle multiple classes. It turns out that one can lose consistency in generalizing a binary classification method to deal with multiple classes. We study a rich family of multiclass methods and provide a necessary and sufficient condition for their consistency. We illustrate our approach by applying it to some multiclass methods proposed in the literature.