10302 Summary – Learning paradigms in dynamic environments

Title10302 Summary – Learning paradigms in dynamic environments
Publication TypeConference Papers
Year of Publication2010
AuthorsHammer, B, Hitzler, P, Maass, W, Toussaint, M
EditorHammer, B, Hitzler, P, Maass, W, Toussaint, M
Conference NameLearning paradigms in dynamic environments
PublisherSchloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany
Conference LocationDagstuhl, Germany

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.