Stream Quantiles via Maximal Entropy Histograms
dc.contributor.author | Arandjelovic, O. | |
dc.contributor.author | Pham, DucSon | |
dc.contributor.author | Venkatesh, S. | |
dc.contributor.editor | Loo, C.K. | |
dc.contributor.editor | Keem Siah, Y. | |
dc.contributor.editor | Wong, K.K.W. | |
dc.contributor.editor | Beng Jin, A.T. | |
dc.contributor.editor | Huang, K. | |
dc.date.accessioned | 2017-03-15T22:05:26Z | |
dc.date.available | 2017-03-15T22:05:26Z | |
dc.date.created | 2017-02-24T00:09:32Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Arandjelovic, 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.uri | http://hdl.handle.net/20.500.11937/49483 | |
dc.identifier.doi | 10.1007/978-3-319-12640-1_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.publisher | Springer | |
dc.subject | video surveillance | |
dc.subject | entropy | |
dc.subject | stream data | |
dc.subject | histogram | |
dc.subject | algorithms | |
dc.subject | quantile estimation | |
dc.title | Stream Quantiles via Maximal Entropy Histograms | |
dc.type | Conference Paper | |
dcterms.source.startPage | 327 | |
dcterms.source.endPage | 334 | |
dcterms.source.title | Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. | |
dcterms.source.series | Neural 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.isbn | 978-3-319-12639-5 | |
dcterms.source.conference | The 21st International Conference on Neural Information Processing (ICONIP 2014) | |
dcterms.source.conference-start-date | Nov 3 2014 | |
dcterms.source.conferencelocation | Kuching, Malaysia | |
dcterms.source.place | Switzerland | |
curtin.department | Department of Computing | |
curtin.accessStatus | Fulltext not available |
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