License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
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DOI: 10.4230/DagSemProc.10302.2
URN: urn:nbn:de:0030-drops-28027
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Hammer, Barbara ; Hitzler, Pascal ; Maass, Wolfgang ; Toussaint, Marc

10302 Summary -- Learning paradigms in dynamic environments

10302.SWM.Paper.2802.pdf (0.2 MB)


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.

BibTeX - Entry

  author =	{Hammer, Barbara and Hitzler, Pascal and Maass, Wolfgang and Toussaint, Marc},
  title =	{{10302 Summary – Learning paradigms in dynamic environments}},
  booktitle =	{Learning paradigms in dynamic environments},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2010},
  volume =	{10302},
  editor =	{Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-28027},
  doi =		{10.4230/DagSemProc.10302.2},
  annote =	{Keywords: Summary}

Keywords: Summary
Collection: 10302 - Learning paradigms in dynamic environments
Issue Date: 2010
Date of publication: 05.11.2010

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