Di Marzio, M and Taylor, CC (2005) Kernel density classification and boosting: an L2 sub analysis. Statistics and Computing, 15 (2). 113 - 123 . ISSN 0960-3174Full text available as:
Available under License : See the attached licence file.
Kernel density estimation is a commonly used approach to classification. However, most of the theoretical results for kernel methods apply to estimation per se and not necessarily to classification. In this paper we show that when estimating the difference between two densities, the optimal smoothing parameters are increasing functions of the sample size of the complementary group, and we provide a small simluation study which examines the relative performance of kernel density methods when the final goal is classification. A relative newcomer to the classification portfolio is “boosting”, and this paper proposes an algorithm for boosting kernel density classifiers. We note that boosting is closely linked to a previously proposed method of bias reduction in kernel density estimation and indicate how it will enjoy similar properties for classification. We show that boosting kernel classifiers reduces the bias whilst only slightly increasing the variance, with an overall reduction in error. Numerical examples and simulations are used to illustrate the findings, and we also suggest further areas of research.
|Copyright, Publisher and Additional Information:||© 2005, Springer Verlag. This is an author produced version of a paper published in Statistics and Computing. Uploaded in accordance with the publisher's self-archiving policy.|
|Keywords:||simulation, discrimination, Cross-validation, Nonparametric Density Estimation, smoothing|
|Academic Units:||The University of Leeds > Faculty of Maths and Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds)|
|Depositing User:||Symplectic Publications|
|Date Deposited:||01 Nov 2012 14:51|
|Last Modified:||08 Feb 2013 17:40|
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