In this paper we apply harmony data smoothing learning on a weighted kernel density model to obtain a sparse density estimator. We empirically compare this method with the least squares cross-validation (LSCV) method for the classical kernel density estimator. The most remarkable result of our study is that the harmony data smoothing learning method outperforms LSCV method in most cases and the support vectors selected by harmony data smoothing learning method are located in the regions of local highest density of the sample. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Hu, X., & Yang, Y. (2007). Investigation on sparse kernel density estimator via harmony data smoothing learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4491 LNCS, pp. 1211–1220). Springer Verlag. https://doi.org/10.1007/978-3-540-72383-7_141
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