Maximum leave-one-out likelihood for kernel density estimation
Abstract
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.
URI
http://www.prasa.org/proceedings/2010/prasa2010-04.pdfhttps://www.researchgate.net/publication/228960852_Maximum_Leave-one-out_Likelihood_for_Kernel_Density_Estimation
http://hdl.handle.net/10394/26552
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