CG Learning
Computational Geometric Learning
| Coordinatore | FRIEDRICH-SCHILLER-UNIVERSITAT JENA
Organization address
address: Ernst-Abbe-Platz 2 contact info |
| Nazionalità Coordinatore | Germany [DE] |
| Totale costo | 3˙159˙561 € |
| EC contributo | 2˙444˙728 € |
| Programma | FP7-ICT
Specific Programme "Cooperation": Information and communication technologies |
| Code Call | FP7-ICT-2009-C |
| Funding Scheme | CP |
| Anno di inizio | 2010 |
| Periodo (anno-mese-giorno) | 2010-11-01 - 2013-10-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
FRIEDRICH-SCHILLER-UNIVERSITAT JENA
Organization address
address: Ernst-Abbe-Platz 2 contact info |
DE (Jena) | coordinator | 0.00 |
| 2 |
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZURICH
Organization address
address: Raemistrasse contact info |
CH (ZUERICH) | participant | 0.00 |
| 3 |
FREIE UNIVERSITAET BERLIN
Organization address
address: Kaiserswertherstrasse contact info |
DE (BERLIN) | participant | 0.00 |
| 4 |
INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE
Organization address
address: Domaine de Voluceau, Rocquencourt contact info |
FR (LE CHESNAY Cedex) | participant | 0.00 |
| 5 |
NATIONAL AND KAPODISTRIAN UNIVERSITY OF ATHENS
Organization address
address: CHRISTOU LADA contact info |
EL (ATHENS) | participant | 0.00 |
| 6 |
RIJKSUNIVERSITEIT GRONINGEN
Organization address
address: Broerstraat contact info |
NL (GRONINGEN) | participant | 0.00 |
| 7 |
TECHNISCHE UNIVERSITAET DORTMUND
Organization address
address: AUGUST-SCHMIDT-STRASSE contact info |
DE (DORTMUND) | participant | 0.00 |
| 8 |
TEL AVIV UNIVERSITY
Organization address
address: RAMAT AVIV contact info |
IL (TEL AVIV) | participant | 0.00 |
Mappa
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Obiettivo del progetto (Objective)
High dimensional geometric data are ubiquitous in science and engineering, and thus processing and analyzing them is a core task in these disciplines. The Computational Geometric Learning project (CG Learning) aims at extending the success story of geometric algorithms with guarantees, as achieved in the CGAL library and the related EU funded research projects, to spaces of 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 plan 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. The following two kinds of approximation guarantee are particularly desirable: first, the solution approximates an objective better if more time and memory resources are employed (algorithmic guarantee), and second, the approximation gets better when the data become more dense and/or more accurate (learning theoretic guarantee). To lay the foundation of a new field---computational geometric learning---we will follow an approach integrating both theoretical and practical developments, the latter in the form of the construction of a high quality software library and application software.