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dc.contributor.author Van der Walt, Christiaan M
dc.contributor.author Barnard, E
dc.date.accessioned 2012-02-14T15:15:52Z
dc.date.available 2012-02-14T15:15:52Z
dc.date.issued 2009-11
dc.identifier.citation Van der Walt, C and Barnard, E. Density estimation from local structure. 20th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Stellenbosch, South Africa, 30 November-01 December 2009, pp 131-136 en_US
dc.identifier.isbn 978-0-7992-2356-9
dc.identifier.uri http://www.dip.ee.uct.ac.za/prasa/PRASA2010/proceedings/2009/prasa09-23.pdf
dc.identifier.uri http://www.dip.ee.uct.ac.za/prasa/PRASA2010/proceedings/2009/
dc.identifier.uri http://hdl.handle.net/10204/5568
dc.description 20th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Stellenbosch, South Africa, 30 November-01 December 2009 en_US
dc.description.abstract The authors propose a hyper-ellipsoid clustering algorithm that grows clusters from local structures in a dataset and estimates the underlying geometrical structure of data with a set of hyper-ellipsoids. The clusters are used to estimate a Gaussian Mixture Model (GMM) density function of the data and the log-likelihood scores are compared to the scores of a GMM trained with the expectation maximization (EM) algorithm on 5 real-world classification datasets (from the UCI collection). They show that their approach gives better generalization performance on unseen test sets for 4 of the 5 datasets considered. en_US
dc.language.iso en en_US
dc.publisher PRASA en_US
dc.subject Density estimation en_US
dc.subject Hyper-ellipsoids en_US
dc.subject Gaussian mixture model (GMM) en_US
dc.subject Local structure en_US
dc.title Density estimation from local structure en_US
dc.type Presentation en_US


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