As mentioned in the introduction, the shortage of labeled **data** is a fundamental problem for applied machine learning. It is important enough that several areas of research is devoted to various aspects of this problem. In the active learning paradigm, labels are acquired in an interactive fashion to maximize the benefit of each new label [Kapoor et al., 2007]. Related approaches include [Branson et al., 2011], where a 'human-in-the-loop' determines which labels to update, thus making the 'most' out of the acquired labels. Crowd sourcing through, e.g. Amazon mechanical turk (mTurk), allows for rapid collection of large amounts of labels, and much research is devoted to the efficient distribution of tasks and the interpretation and weighting of retrieved labels [Welinder et al., 2010]. Further areas include weakly supervised method, e.g. multiple instance learning [Dietterich et al., 1997] or latent structureal SVMs [Yu and Joachims, 2009] where the level of supervision is lower than the given task demands. Other approaches include semi-supervised learning that make use of small amounts of labelled **data** together with large amounts of unlabeled data. Notably the concept of co-training [Blum and Mitchell, 1998] is a popular approach.