Algorithms for clustering data
Algorithms for clustering data
Machine Learning
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Fingerprint classification: a review
Pattern Analysis & Applications
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Artificial Intelligence in Medicine
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Effective fingerprint classification by localized models of support vector machines
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Support vector machine (SVM) which was originally designed for binary classification has achieved superior performance in various classification problems. In order to extend it to multiclass classification, one popular approach is to consider the problem as a collection of binary classification problems. Majority voting or winner-takes-all is then applied to combine those outputs, but it often causes problems to consider tie-breaks and tune the weights of individual classifiers. This paper presents two novel ensemble approaches: probabilistic ordering of one-vs-rest (OVR) SVMs with naïve Bayes classifier and multiple decision templates of OVR SVMs. Experiments with multiclass datasets have shown the usefulness of the ensemble methods.