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dc.contributor.authorBarnard, Etienne
dc.date.accessioned2018-03-07T10:01:49Z
dc.date.available2018-03-07T10:01:49Z
dc.date.issued2010
dc.identifier.citationEtienne Barnard, “Maximum leave-one-out likelihood for kernel density estimation”, in Proc. Annual Symp. Pattern Recognition Association of South Africa (PRASA), pp 19-24, Stellenbosch, South Africa, 2010. [http://engineering.nwu.ac.za/multilingual-speech-technologies-must/publications]en_US
dc.identifier.urihttp://www.prasa.org/proceedings/2010/prasa2010-04.pdf
dc.identifier.urihttps://www.researchgate.net/publication/228960852_Maximum_Leave-one-out_Likelihood_for_Kernel_Density_Estimation
dc.identifier.urihttp://hdl.handle.net/10394/26552
dc.description.abstractWe investigate the application of kernel density estimators to pattern-recognition problems. These estimators have a number of attractive properties for data analysis in pattern recognition, but the particular characteristics of patternrecognition problems also place some non-trivial requirements on kernel density estimation – especially on the algorithm used to compute bandwidths. We introduce a new algorithm for variable bandwidth estimation, investigate some of its properties, and show that it performs competitively on a wide range of tasks, particularly in spaces of high dimensionality.en_US
dc.description.sponsorshipMultilingual Speech Technologies Group, North-West University, Vanderbijlpark, South Africaen_US
dc.language.isoenen_US
dc.publisherPattern Recognition Association of South Africa and Mechatronics International Conferenceen_US
dc.subjectMaximum Leave-one-out Likelihooden_US
dc.subjectKernel Density Estimationen_US
dc.subjectPattern-recognition problemsen_US
dc.subjectAlgorithm for variable bandwidth estimationen_US
dc.titleMaximum leave-one-out likelihood for kernel density estimationen_US
dc.typePresentationen_US


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