Show simple item record

dc.contributor.authorArandjelovic, O.
dc.contributor.authorPham, DucSon
dc.contributor.authorVenkatesh, S.
dc.contributor.editorLoo, C.K., Keem Siah, Y., Wong, K.K.W., Beng Jin, A.T. & Huang, K.
dc.date.accessioned2017-03-15T22:05:26Z
dc.date.available2017-03-15T22:05:26Z
dc.date.created2017-02-24T00:09:32Z
dc.date.issued2014
dc.identifier.citationArandjelovic, O. and Pham, D. and Venkatesh, S. 2014. Stream Quantiles via Maximal Entropy Histograms, in Loo, C.K., Keem Siah, Y., Wong, K.K.W., Beng Jin, A.T. & Huang, K. (ed), The 21st International Conference on Neural Information Processing (ICONIP 2014), Nov 3 2014, pp. 327-334. Kuching, Malaysia: Springer.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/49483
dc.identifier.doi10.1007/978-3-319-12640-1_40
dc.description.abstract

We address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited. We (i) highlight the limitations of approaches previously described in the literature which make them unsuitable for non-stationary streams, (ii) describe a novel principle for the utilization of the available storage space, and (iii) introduce two novel algorithms which exploit the proposed principle. Experiments on three large real-world data sets demonstrate that the proposed methods vastly outperform theexisting alternatives.

dc.publisherSpringer
dc.subjectvideo surveillance
dc.subjectentropy
dc.subjectstream data
dc.subjecthistogram
dc.subjectalgorithms
dc.subjectquantile estimation
dc.titleStream Quantiles via Maximal Entropy Histograms
dc.typeConference Paper
dcterms.source.startPage327
dcterms.source.endPage334
dcterms.source.titleNeural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835.
dcterms.source.seriesNeural Information Processing, 21st International Conference, ICONIP 2014, Kuching, Malaysia, November 3-6, 2014. Proceedings, Part II: Lecture Notes in Computer Science Volume 8835 2014
dcterms.source.isbn978-3-319-12639-5
dcterms.source.conferenceThe 21st International Conference on Neural Information Processing (ICONIP 2014)
dcterms.source.conference-start-dateNov 3 2014
dcterms.source.conferencelocationKuching, Malaysia
dcterms.source.placeSwitzerland
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record