Maximum leave-one-out likelihood for kernel density estimation

We investigate the application of kernel density estimators to pattern-recognition problems. These estima tors 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 use d 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.