This paper studies issues relating to the parameterization of probability distributions over binary data sets. Several such parameterizations of models for binary data are known, ...
David Buchman, Mark W. Schmidt, Shakir Mohamed, Da...
Most real-world data is stored in relational form. In contrast, most statistical learning methods work with "flat" data representations, forcing us to convert our data i...
Lise Getoor, Nir Friedman, Daphne Koller, Benjamin...
Abstract. Existing relational learning approaches usually work on complete relational data, but real-world data are often incomplete. This paper proposes the MGDA approach to learn...
In this paper we address the object recognition problem in a probabilistic framework to detect and describe object appearance through image features organized by means of active c...
Abstract. Since the early days of generation research, it has been acknowledged that modeling the global structure of a document is crucial for producing coherent, readable output....