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Article
Open AccessScalable variable selection for two-view learning tasks with projection operators
In this paper we propose a novel variable selection method for two-view settings, or for vector-valued supervised learning problems. Our framework is able to handle extremely large scale selection tasks, where...
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Article
Multi-view kernel completion
In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, with help of information from other i...
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Article
Multilabel classification through random graph ensembles
We present new methods for multilabel classification, relying on ensemble learning on a collection of random output graphs imposed on the multilabel, and a kernel-based structured output learner as the base cl...
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Article
General and Efficient Multisplitting of Numerical Attributes
Often in supervised learning numerical attributes require special treatment and do not fit the learning scheme as well as one could hope. Nevertheless, they are common in practical tasks and, therefore, need t...