Barnard, Etienne2018-03-072018-03-072010Etienne 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]http://www.prasa.org/proceedings/2010/prasa2010-04.pdfhttps://www.researchgate.net/publication/228960852_Maximum_Leave-one-out_Likelihood_for_Kernel_Density_Estimationhttp://hdl.handle.net/10394/26552We 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.enMaximum Leave-one-out LikelihoodKernel Density EstimationPattern-recognition problemsAlgorithm for variable bandwidth estimationPresentation