Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Pairwise classification and support vector machines
Advances in kernel methods
Three learning phases for radial-basis-function networks
Neural Networks
Comparison of multiclass SVM decomposition schemes for visual object recognition
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Hierarchical neural networks utilising dempster-shafer evidence theory
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Computer Speech and Language
The effect of fuzzy training targets on voice quality classification
MPRSS'12 Proceedings of the First international conference on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
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Support vector machines (SVM) are learning algorithms derived from statistical learning theory. The SVMapproac h was originally developed for binary classification problems. In this paper SVMarc hitectures for multi-class classification problems are discussed, in particular we consider binary trees of SVMs to solve the multi-class problem. Numerical results for different classifiers on a benchmark data set of handwritten digits are presented.