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BEGIN:VEVENT
UID:/NewsandEvents/Archives/2003/newsitem/530/22-O
ctober-2003-Learning-Solutions-2003-Adaptive-Intel
ligence-in-research-and-practical-applications-Rad
boud-Auditorium-Nijmegen-the-Netherlands
DTSTAMP:20030926T000000
SUMMARY:Learning Solutions 2003: Adaptive Intellig
ence in research and practical applications, Radbo
ud Auditorium, Nijmegen, the Netherlands
DTSTART;VALUE=DATE:20031022
DTEND;VALUE=DATE:20031022
LOCATION:Radboud Auditorium, Nijmegen, the Netherl
ands
DESCRIPTION:Neural networks are computer programs
that are able to learn. Their functioning is inspi
red by the function of the brain. The value added
by neural networks is strongest for those problems
that lack explicit knowledge. A large number of n
eural network aided applications has already been
realized. Well-known applications are pattern reco
gnition, time series prediction, and process contr
ol. Neural networks do not always produce the best
solution, however. Better solutions are therefore
often obtained through a combination with explici
t domain knowledge. Bayesian statistics offers an
elegant formalism to combine learning and explicit
modeling. Furthermore, statistical methods for qu
antification of reliability are of great importanc
e. A modern trend is therefore marked by an integr
ated approach that combines neural networks with m
ethods from statistics and artificial intelligence
. The symposium 'Learning Solutions 2003' offers a
n up-to-date overview of Dutch research in this ar
ea. For more information, see http://www.snn.kun
.nl/nederland/index.php3?page=15
X-ALT-DESC;FMTTYPE=text/html:\n \nNeural n
etworks are computer programs that are able to lea
rn.\nTheir functioning is inspired by the function
of the brain. \nThe value added by neural network
s is strongest for those \nproblems that lack expl
icit knowledge.\n A large number of neural network
aided applications has already been realized. \nW
ell-known applications are pattern recognition, ti
me series prediction,\n and process control.\n Neu
ral networks do not always produce the best soluti
on, however. \nBetter solutions are therefore ofte
n obtained through a combination\n with explicit d
omain knowledge.\n Bayesian statistics offers an e
legant formalism to combine learning \nand explici
t modeling. \nFurthermore, statistical methods for
quantification of reliability \nare of great impo
rtance. \nA modern trend is therefore marked by an
integrated approach that \ncombines neural networ
ks with methods from statistics and \nartificial i
ntelligence. \nThe symposium 'Learning Solutions 2
003' offers an up-to-date \noverview of Dutch rese
arch in this area.\n

\n \n For
more information, see\n http://www.snn.kun.nl/nederland/index.php
3?page=15\n

URL:/NewsandEvents/Archives/2003/newsitem/530/22-O
ctober-2003-Learning-Solutions-2003-Adaptive-Intel
ligence-in-research-and-practical-applications-Rad
boud-Auditorium-Nijmegen-the-Netherlands
END:VEVENT
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