Computational Geometric Learning - CG Learning

The Computational Geometric Learning project aims at extending the success story of geometric algorithms with guarantees to high-dimensions. This is not a straightforward task. For many problems, no efficient algorithms exist that compute the exact solution in high dimensions. This behavior is commonly called the curse of dimensionality. We try to address the curse of dimensionality by focusing on inherent structure in the data like sparsity or low intrinsic dimension, and by resorting to fast approximation algorithms.

Antragsteller Joachim Giesen
Lehrstuhl Lehrstuhl Theoretische Informatik II
Drittmittelgeber EU
Laufzeit Oktober 2010 - Oktober 2013
Stellen 1
Besetzung Thomas Baumbach
Lars Kühne
Projektart EU Verbundprojekt
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