Approximate K-Means++ in Sublinear Time
dc.contributor.author
Bachem, Olivier
dc.contributor.author
Lucic, Mario
dc.contributor.author
Hassani, S. Hamed
dc.contributor.author
Krause, Andreas
dc.date.accessioned
2017-06-12T13:40:36Z
dc.date.available
2017-06-12T13:40:36Z
dc.date.issued
2016
dc.identifier.uri
http://hdl.handle.net/20.500.11850/121216
dc.language.iso
en
dc.publisher
AAAI
dc.subject
Clustering
dc.subject
K-Means
dc.subject
Large-scale machine learning
dc.subject
Markov Chain Monte Carlo
dc.subject
Approximate sampling
dc.title
Approximate K-Means++ in Sublinear Time
dc.type
Conference Paper
ethz.book.title
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)
ethz.pages.start
1459
ethz.pages.end
1467
ethz.event
30th AAAI Conference on Artificial Intelligence (AAAI 2016)
ethz.event.location
Phoenix, AZ, USA
ethz.event.date
February 12-17, 2016
ethz.publication.place
Phoenix, AZ
ethz.publication.status
published
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03908 - Krause, Andreas / Krause, Andreas
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::03908 - Krause, Andreas / Krause, Andreas
ethz.date.deposited
2017-06-12T13:45:00Z
ethz.source
ECIT
ethz.identifier.importid
imp593654c27e66d24193
ethz.ecitpid
pub:183310
ethz.eth
yes
ethz.availability
Metadata only
ethz.rosetta.installDate
2017-07-15T03:42:57Z
ethz.rosetta.lastUpdated
2024-02-02T00:25:49Z
ethz.rosetta.versionExported
true
ethz.COinS
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Conference Paper [34961]