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dc.contributor.author
Rupprechter, Benjamin
dc.date.accessioned
2017-06-13T16:41:23Z
dc.date.available
2017-06-13T16:41:23Z
dc.date.issued
2012
dc.identifier.uri
http://hdl.handle.net/20.500.11850/153452
dc.identifier.doi
10.3929/ethz-a-007305105
dc.format
application/pdf
dc.language.iso
en
dc.publisher
ETH Zürich, Department of Computer Science, Learning & Adaptive Systems Group
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
PROGRAMME UND ALGORITHMEN ZUR LÖSUNG SPEZIELLER PROBLEME
dc.subject
BAYES'SCHE NETZWERKE (MATHEMATISCHE LOGIK)
dc.subject
MASCHINELLES LERNEN (KÜNSTLICHE INTELLIGENZ)
dc.subject
PROGRAMS AND ALGORITHMS FOR THE SOLUTION OF SPECIAL PROBLEMS
dc.subject
BAYESIAN NETWORKS (MATHEMATICAL LOGIC)
dc.subject
MACHINE LEARNING (ARTIFICIAL INTELLIGENCE)
dc.title
Noisy Active Learning from a Bayesian Perspective
dc.type
Master Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2012
ethz.size
Online-Ressource
ethz.code.ddc
DDC - DDC::0 - Computer science, information & general works::004 - Data processing, computer science
ethz.code.ddc
DDC - DDC::0 - Computer science, information & general works::004 - Data processing, computer science
ethz.notes
Master Thesis ETH Zürich, 2012.
ethz.identifier.nebis
007305105
ethz.publication.place
Zürich
ethz.publication.status
published
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science
ethz.date.deposited
2017-06-13T16:51:48Z
ethz.source
ECOL
ethz.identifier.importid
imp59366b2503dfd58846
ethz.ecolpid
eth:5604
ethz.eth
yes
ethz.availability
Open access
ethz.rosetta.installDate
2017-07-26T17:42:31Z
ethz.rosetta.lastUpdated
2020-02-15T04:17:06Z
ethz.rosetta.versionExported
true
ethz.COinS
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