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Maximum leave-one-out likelihood for kernel density estimation

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Barnard, Etienne

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Pattern Recognition Association of South Africa and Mechatronics International Conference

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We 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.

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Etienne 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]

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