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Stance Classification of Context-Dependent Claims
Recent work has addressed the problem of detecting relevant claims for a given controversial topic.
Electricity Demand Forecasting by Multi-Task Learning
We explore the application of kernel-based multi-task learning techniques to forecast the demand of electricity in multiple nodes of a distribution network.
Learning from Distributions via Support Measure Machines
This paper presents a kernel-based discriminative learning framework on probability measures.
The representer theorem for Hilbert spaces: a necessary and sufficient condition
In particular, the main result of this paper implies that, for a sufficiently large family of regularization functionals, radial nondecreasing functions are the only lower semicontinuous regularization terms that guarantee existence of a representer theorem for any choice of the data.
