Reinforcement learning addresses the problem of learning to select actions in order to maximize one's performance inunknownenvironments. Toscale reinforcement learning to com...
In many practical domains, misclassification costs can differ greatly and may be represented by class ratios, however, most learning algorithms struggle with skewed class distrib...
William Klement, Peter A. Flach, Nathalie Japkowic...
Training a good text detector requires a large amount of labeled data, which can be very expensive to obtain. Cotraining has been shown to be a powerful semi-supervised learning t...
Multiple phenomena often diffuse through a social network, sometimes in competition with one another. Product adoption and political elections are two examples where network diffus...
Matthias Broecheler, Paulo Shakarian, V. S. Subrah...
Ink-bleed interference is a serious problem that affects the legibility of old documents. Ink-bleed can be reduced reasonably well using pixel classification based on user supplie...