Machine Learning - Special issue on inductive transfer
Support vector machines applied to face recognition
Proceedings of the 1998 conference on Advances in neural information processing systems II
Probabilistic Discriminative Kernel Classifiers for Multi-class Problems
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Comparison of Face Verification Results on the XM2VTS Database
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
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
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Laplacian Support Vector Machines Trained in the Primal
The Journal of Machine Learning Research
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The extension of kernel-based binary classifiers to multiclass problems has been approached with different strategies in the last decades. Nevertheless, the most frequently used schemes simply rely on different criteria to combine the decisions of a set of independently trained binary classifiers. In this paper we propose an approach that aims at establishing a connection in the training stage of the classifiers using an innovative criterion. Motivated by the increasing interest in the semisupervised learning framework, we describe a soft-constraining scheme that allows us to include probabilistic constraints on the outputs of the classifiers, using the unlabeled training data. Embedding this knowledge in the learning process can improve the generalization capabilities of the multiclass classifier, and it leads to a more accurate approximation of a probabilistic output without an explicit post-processing. We investigate our intuition on a face identification problem with 295 classes.