The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
On Learning Vector-Valued Functions
Neural Computation
On the Consistency of Multiclass Classification Methods
The Journal of Machine Learning Research
VC Theory of Large Margin Multi-Category Classifiers
The Journal of Machine Learning Research
MSVMpack: A Multi-Class Support Vector Machine Package
The Journal of Machine Learning Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
MSVMpack: A Multi-Class Support Vector Machine Package
The Journal of Machine Learning Research
Estimating the class posterior probabilities in protein secondary structure prediction
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Cascading discriminant and generative models for protein secondary structure prediction
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
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Roughly speaking, there is one main model of pattern recognition support vector machine, with several variants of lower popularity. On the contrary, among the different multi-class support vector machines which can be found in literature, none is clearly favoured. On the one hand, they exhibit distinct statistical properties. On the other hand, multiple comparative studies between multi-class support vector machines and decomposition methods have highlighted the fact that in practice, each model has its advantages and drawbacks. In this article, we introduce a generic model of multi-class support vector machine. It provides the first unifying definition of all the machines of this kind published so far. This contribution makes it possible to devise new machines meeting specific requirements as well as to analyse globally the statistical properties of the multi-class support vector machines.