Uncertainty is a popular phenomenon in machine learning and a variety of methods to model uncertainty at different levels has been developed. The aim of this paper is to motivate ...
Traditionally, Information Extraction (IE) has focused on satisfying precise, narrow, pre-specified requests from small homogeneous corpora (e.g., extract the location and time o...
Michele Banko, Michael J. Cafarella, Stephen Soder...
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Abstract. Semantic wikis have been successfully applied to many problems in knowledge management and collaborative authoring. They are particularly appropriate for scientific and m...
Christoph Lange 0002, Sean McLaughlin, Florian Rab...
Embedded Peer-to-Peer Systems (EP2P) represent a new challenge in the development of software for distributed systems. The main objective of the SMEPP (Secure Middleware for Embed...