<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Barbara Hammer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Wolfgang Maass</style></author><author><style face="normal" font="default" size="100%">Marc Toussaint</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">10302 Abstracts Collection - Learning paradigms in dynamic environments</style></title><secondary-title><style face="normal" font="default" size="100%">Learning paradigms in dynamic environments</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autonomous learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural-symbolic integration</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurobiology</style></keyword><keyword><style  face="normal" font="default" size="100%">Recurrent neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Speech processing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://drops.dagstuhl.de/opus/volltexte/2010/2804</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany</style></publisher><pub-location><style face="normal" font="default" size="100%">Dagstuhl, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Hammer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Wolfgang Maass</style></author><author><style face="normal" font="default" size="100%">Marc Toussaint</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Barbara Hammer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Wolfgang Maass</style></author><author><style face="normal" font="default" size="100%">Marc Toussaint</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">10302 Summary – Learning paradigms in dynamic environments</style></title><secondary-title><style face="normal" font="default" size="100%">Learning paradigms in dynamic environments</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://drops.dagstuhl.de/opus/volltexte/2010/2802</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">10302</style></number><publisher><style face="normal" font="default" size="100%">Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany</style></publisher><pub-location><style face="normal" font="default" size="100%">Dagstuhl, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Marco Gori</style></author><author><style face="normal" font="default" size="100%">Barbara Hammer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Guenther Palm</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Perspectives and challenges for recurrent neural network training</style></title><secondary-title><style face="normal" font="default" size="100%">Logic Journal of the IGPL</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1093/jigpal/jzp042</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">617–619</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>