Maximum leave-one-out likelihood for kernel density estimation
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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.
- Faculty of Engineering