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SUMMARY:Statistical learning theory for scientific applications: an overvi
ew
DTSTART;VALUE=DATE-TIME:20181004T143000Z
DTEND;VALUE=DATE-TIME:20181004T145000Z
DTSTAMP;VALUE=DATE-TIME:20201126T104407Z
UID:indico-contribution-28910@agenda.infn.it
DESCRIPTION:Speakers: JESUS VEGA (Laboratorio Nacional de FusiĆ³n. CIEMAT)
\nThe statistical learning theory allows the estimation of functional depe
ndency from a given collection of data. It includes discriminant analysis\
, regression analysis and the density estimation problem. It is used to ob
tain data-driven models to relate heterogeneous quantities with the aim of
making predictions. In science\, statistical learning is an essential the
ory to find relationships among quantities whose formulation cannot be ded
uced from first principles. This talk summarizes the use of statistical le
arning methods in scientific problems: from unsupervised techniques to sup
ervised techniques\, from simple predictions to probabilistic predictions\
, from non-parametric estimations to parametric estimations\, from real-ti
me needs to off-line needs and from standard datasets of information to pr
ivileged datasets of information. Concepts and examples will be given.\n\n
https://agenda.infn.it/event/15217/contributions/28910/
LOCATION:INFN-LNF\, Italy Bruno Touschek Auditorium
URL:https://agenda.infn.it/event/15217/contributions/28910/
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