Information Geometry and its Applications III

Abstract Richard Nock

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Richard Nock  (Université des Antilles et de la Guyane, France)
Thursday, August 05, 2010, room Hörsaal 2
The information geometry of boosting algorithms

The last two decades have seen the birth and boost of a new category of supervised learning algorithms, known as boosting algorithms. This family has gradually appeared as much more pervasive than initially expected, with applications to the induction of virtually any kind of classifier. While the first lenses used to understand the algorithms were essentially grounded in convex optimization, they have been more recently completed by results in information geometry, escaping the traditional Riemannian framework, that help to get a more complete picture of these fascinating algorithmic machineries. The aim of the talk is to present the central geometric part of this picture, which we believe may serve to design more easily new and more efficient boosting algorithms.

Date and Location

August 02 - 06, 2010
University of Leipzig
04103 Leipzig

Scientific Organizers

Nihat Ay
Max Planck Institute for Mathematics in the Sciences
Information Theory of Cognitive Systems Group
Contact by Email

Paolo Gibilisco
Università degli Studi di Roma "Tor Vergata"
Facoltà di Economia
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František Matúš
Academy of Sciences of the Czech Republic
Institute of Information Theory and Automation
Czech Republic
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Scientific Committee

Shun-ichi Amari
Brain Science Institute, Mathematical Neuroscience Laboratory
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Imre Csiszár
Hungarian Academy of Sciences
Alfréd Rényi Institute of Mathematics
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Dénes Petz
Budapest University of Technology and Economics
Department for Mathematical Analysis
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Giovanni Pistone
Collegio Carlo Alberto, Moncalieri
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Administrative Contact

Antje Vandenberg
Max Planck Institute for Mathematics in the Sciences
Contact by Email
Phone: (++49)-(0)341-9959-552
Fax: (++49)-(0)341-9959-555

05.04.2017, 12:42