%0 Conference Proceedings %B Learning paradigms in dynamic environments %D 2010 %T 10302 Abstracts Collection - Learning paradigms in dynamic environments %E Barbara Hammer %E Pascal Hitzler %E Wolfgang Maass %E Marc Toussaint %K Autonomous learning %K Dynamic systems %K Neural-symbolic integration %K Neurobiology %K Recurrent neural networks %K Speech processing %B Learning paradigms in dynamic environments %I Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany %C Dagstuhl, Germany %G eng %U http://drops.dagstuhl.de/opus/volltexte/2010/2804 %0 Conference Paper %B Learning paradigms in dynamic environments %D 2010 %T 10302 Summary – Learning paradigms in dynamic environments %A Barbara Hammer %A Pascal Hitzler %A Wolfgang Maass %A Marc Toussaint %E Barbara Hammer %E Pascal Hitzler %E Wolfgang Maass %E Marc Toussaint %X

The seminar centered around problems which arise in the context of machine learning in dynamic environments. Particular emphasis was put on a couple of specific questions in this context: how to represent and abstract knowledge appropriately to shape the problem of learning in a partially unknown and complex environment and how to combine statistical inference and abstract symbolic representations; how to infer from few data and how to deal with non i.i.d. data, model revision and life-long learning; how to come up with efficient strategies to control realistic environments for which exploration is costly, the dimensionality is high and data are sparse; how to deal with very large settings; and how to apply these models in challenging application areas such as robotics, computer vision, or the web.

%B Learning paradigms in dynamic environments %I Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany %C Dagstuhl, Germany %G eng %U http://drops.dagstuhl.de/opus/volltexte/2010/2802