A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Pairwise classification and support vector machines
Advances in kernel methods
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
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
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
Polychotomous classification with pairwise classifiers: a new voting principle
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Fuzzy Classifier Design
Are artificial neural networks black boxes?
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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At first, support vector machines (SVMs) were applied to solve binary classification problems. They can also be extended to solve multicategory problems by the combination of binary SVM classifiers. In this paper, we propose a new fuzzy model that includes the advantages of several previously published methods solving their drawbacks. For each datum, a class is rejected using information provided by every decision function related to it. Our proposal yields membership degrees in the unit interval and in some cases, it improves the performance of the former methods in the unclassified regions.