Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Fast pixel classification by SVM using vector quantization, tabu search and hybrid color space
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
A new model selection method for SVM
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Speed-Up LOO-CV with SVM classifier
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Multiclass problems with binary SVM classifiers are commonly treated as a decomposition in several binary sub-problems. An open question is how to properly tune all these sub-problems (SVM hyperparameters) in order to have the lowest error rate for a SVM multiclass scheme based on decomposition. In this paper, we propose a new approach to optimize the generalization capacity of such SVM multiclass schemes. This approach consists in a global selection of hyperparameters for sub-problems all together and it is denoted as multimodel selection. A multi-model selection can outperform the classical individual model selection used until now in the literature. An evolutionary algorithm (EA) is proposed to perform multi-model selection. Experimentations with our EA method show the benefits of our approach over the classical one.