Maximum leave-one-out likelihood for kernel density estimation
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Date
Authors
Barnard, Etienne
Journal Title
Journal ISSN
Volume Title
Publisher
Pattern Recognition Association of South Africa and Mechatronics International Conference
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.
Description
Citation
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]