We equate nonlinear dimensionality reduction (NLDR) to graph embedding with side information about the vertices, and derive a solution to either problem in the form of a kernel-ba...
Generalized belief propagation (GBP) has proven to be a promising technique for approximate inference tasks in AI and machine learning. However, the choice of a good set of cluste...
We introduce ClueMaker, the first language designed specifically for approximate record matching. Clues written in ClueMaker predict whether two records denote the same thing based...
Martin Buechi, Andrew Borthwick, Adam Winkel, Arth...
Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multistrategy and multi-source approach to question...
Jennifer Chu-Carroll, Krzysztof Czuba, John M. Pra...
Principal components analysis (PCA) is one of the most widely used techniques in machine learning and data mining. Minor components analysis (MCA) is less well known, but can also...
Max Welling, Felix V. Agakov, Christopher K. I. Wi...