In recent years there has been a lot of interest in designing principled classification algorithms over multiple cues, based on the intuitive notion that using more features shou...
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to obtain state-of-theart perform...
Coordinate gradient learning is motivated by the problem of variable selection and determining variable covariation. In this paper we propose a novel unifying framework for coordi...
When equipped with kernel functions, online learning algorithms are susceptible to the "curse of kernelization" that causes unbounded growth in the model size. To addres...
For many biomedical modelling tasks a number of different types of data may influence predictions made by the model. An established approach to pursuing supervised learning with ...
Yiming Ying, Colin Campbell, Theodoros Damoulas, M...