Pairwise classification and support vector machines
Advances in kernel methods
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Simple Decomposition Method for Support Vector Machines
Machine Learning
Multiclass LS-SVMs: Moderated Outputs and Coding-Decoding Schemes
Neural Processing Letters
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Lagrangian support vector machines
The Journal of Machine Learning Research
The Journal of Machine Learning Research
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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A geometric construction is presented which is shown to be an effective tool for understanding and implementing multi-category support vector classification. It is demonstrated how this construction can be used to extend many other existing two-class kernel-based classification methodologies in a straightforward way while still preserving attractive properties of individual algorithms. Reducing training times through incorporating the results of pairwise classification is also discussed and experimental results presented.