The supervised learning paradigm assumes in general that both training and test data are sampled from the same distribution. When this assumption is violated, we are in the setting...
We present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms u...
David Sontag, Kevyn Collins-Thompson, Paul N. Benn...
Blob trackers have become increasingly powerful in recent years largely due to the adoption of statistical appearance models which allow effective background subtraction and robus...
We propose an integral image based algorithm to extract feature covariance matrices of all possible rectangular regions within a given image. Covariance is an essential indicator ...
Distributed source coding (DSC) depends strongly on accurate knowledge of correlation between sources. Previous works have reported capacity-approaching code constructions when ex...