AGALT

Asymptotic Geometric Analysis and Learning Theory

Coordinatore	TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	Israel [IL]
Totale costo	750˙000 €
EC contributo	750˙000 €
Programma	FP7-IDEAS-ERC Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	ERC-2007-StG
Funding Scheme	ERC-SG
Anno di inizio	2009
Periodo (anno-mese-giorno)	2009-03-01   -   2014-02-28

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY  Organization address address: TECHNION CITY - SENATE BUILDING city: HAIFA postcode: 32000 contact info Titolo: Mr. Nome: Mark Cognome: Davison Email: send email Telefono: +972 4 829 4854 Fax: +972 4 823 2958	IL (HAIFA)	hostInstitution	0.00
2	TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY  Organization address address: TECHNION CITY - SENATE BUILDING city: HAIFA postcode: 32000 contact info Titolo: Prof. Nome: Shahar Cognome: Mendelson Email: send email Telefono: -8293250 Fax: -8292420	IL (HAIFA)	hostInstitution	0.00

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 Obiettivo del progetto (Objective)

'In a typical learning problem one tries to approximate an unknown function by a function from a given class using random data, sampled according to an unknown measure. In this project we will be interested in parameters that govern the complexity of a learning problem. It turns out that this complexity is determined by the geometry of certain sets in high dimension that are connected to the given class (random coordinate projections of the class). Thus, one has to understand the structure of these sets as a function of the dimension - which is given by the cardinality of the random sample. The resulting analysis leads to many theoretical questions in Asymptotic Geometric Analysis, Probability (most notably, Empirical Processes Theory) and Combinatorics, which are of independent interest beyond the application to Learning Theory. Our main goal is to describe the role of various complexity parameters involved in a learning problem, to analyze the connections between them and to investigate the way they determine the geometry of the relevant high dimensional sets. Some of the questions we intend to tackle are well known open problems and making progress towards their solution will have a significant theoretical impact. Moreover, this project should lead to a more complete theory of learning and is likely to have some practical impact, for example, in the design of more efficient learning algorithms.'