Efficient evaluation of XML queries requires the determination of whether a relationship exists between two elements. A number of labeling schemes have been designed to label the ...
We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task da...
An important problem in image labeling concerns learning with images labeled at varying levels of specificity. We propose an approach that can incorporate images with labels drawn...
We introduce a unified graph representation of the Web, which includes both structural and usage information. We model this graph using a simple union of the Web's hyperlink ...
Barbara Poblete, Carlos Castillo, Aristides Gionis
We present a new online learning algorithm in the selective sampling framework, where labels must be actively queried before they are revealed. We prove bounds on the regret of ou...