The mission of the Journal of Machine Learning Gossip (JMLG) is to provide an archival source of important information that is often discussed informally at conferences but is rarely, if ever, written down.
I'm interested in machine learning techniques (graphical models, kernel methods) applied to text understanding (entity and relation extraction, coreference resolution, document classification and clustering, confidence prediction, social network analysis, data mining).
38. H-M. Haav, An Application of Inductive Concept Analysis to Construction of Domain-specific Ontologies, In: B. Thalheim, Gunar Fiedler (Eds), Emerging Database Research in East Europe, Proceedings of the Pre-conference Workshop of VLDB 2003, Computer Science Reports, Brandenburg University of Technology at Cottbus, 2003, 14/3, pp 63-67
The mission of the Journal of Machine Learning Gossip (JMLG) is to provide an archival source of important information that is often discussed informally at conferences but is rarely, if ever, written down.
This is a repository of databases, domain theories and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms.
Within the TENCompetence project we aim to develop and integrate models and tools into an open source infrastructure for the creation, storage and exchange of learning objects, suitable knowledge resources as well as learning experiences. This paper analyzes the potential of social software tools for providing part of the required functionality using a detailed scenario. It then discusses the challenges involved, focusing on interoperability, identity management and providing the right Web 2.0 tools for the required functionalities. Finally, we sketch a possible infrastructure based on Facebook, providing information propagation along a social network graph.