The nature of statistical learning theory
The nature of statistical learning theory
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Multi-Classification by Using Tri-Class SVM
Neural Processing Letters
Evaluating feature selection for SVMs in high dimensions
ECML'06 Proceedings of the 17th European conference on Machine Learning
Comparison of multiclass SVM decomposition schemes for visual object recognition
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
IEEE Transactions on Neural Networks
Experiments with Supervised Fuzzy LVQ
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Application of Self Organizing Maps to multi-resolution and multi-spectral remote sensed images
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Comparison of Neural Classification Algorithms Applied to Land Cover Mapping
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Fuzzy Gaussian Process Classification Model
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Studying self- and active-training methods for multi-feature set emotion recognition
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
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
A fuzzy classifier to deal with similarity between labels on automatic prosodic labeling
Computer Speech and Language
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
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We present a novel approach for Fuzzy-Input Fuzzy-Output classification. One-Against-All Support Vector Machines are adapted to deal with the fuzzy memberships encoded in fuzzy labels, and to also give fuzzy classification answers. The mathematical background for the modifications is given. In a benchmark application, the recognition of emotions in human speech, the accuracy of our F2-SVM approach is clearly superior to that of fuzzy MLP and fuzzy K-NN architectures.