Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. Th...
This paper introduces a strategy for training ensemble classifiers by analysing boosting within margin theory. We present a bound on the generalisation error of ensembled classifi...
Huma Lodhi, Grigoris J. Karakoulas, John Shawe-Tay...
Learning from ambiguous training data is highly relevant in many applications. We present a new learning algorithm for classification problems where labels are associated with se...
Object detection can be posted as those classification tasks where the rare positive patterns are to be distinguished from the enormous negative patterns. To avoid the danger of m...
In this paper, we propose a novel tree-structured multi-view face detector (MVFD), which adopts the coarse-to-fine strategy to divide the entire face space into smaller and smalle...