Machine Learning - Special issue on inductive transfer
Learning to learn
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
A model of inductive bias learning
Journal of Artificial Intelligence Research
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In the statistical pattern recognition field the number of samples to train a classifier is usually insufficient. Nevertheless, it has been shown that some learning domains can be divided in a set of related tasks, that can be simultaneously trained sharing information among the different tasks. This methodology is known as the multi-task learning paradigm. In this paper we propose a multi-task probabilistic logistic regression model and develop a learning algorithm based in this framework, which can deal with the small sample size problem. Our experiments performed in two independent databases from the UCI and a multi-task face classification experiment show the improved accuracies of the multi-task learning approach with respect to the single task approach when using the same probabilistic model.