The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M of basis-function coefficients associated with B.Expand

We investigate the problem of learning logistic-regression models for multiple classification tasks, where the training data set for each task is not drawn from the same statistical distribution.Expand

A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sample… Expand

We introduce an auxiliary variable μ for each example in <i>D<sup>a</sup></i> to reflect its mismatch with Dp and propose a method to correct the sample-selection bias.Expand

In analyzing data from multiple related studies, it often is of interest to borrow information across studies and to cluster similar studies. Although parametric hierarchical models are commonly… Expand

We introduce a set of relevance parameters that control the degree to which data from other tasks are used in estimating the current task's classifier parameters.Expand