Kernel density classification and boosting: an L2 analysis
Kernel density classification and boosting: an L2 analysis
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DOI：
10.1007/s11222-005-6203-8
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发表时间：
2005-04
影响因子：
2.2
通讯作者：
M. Marzio;C. Taylor
中科院分区：
文献类型：
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作者：
M. Marzio;C. Taylor
核密度估计是一种常用的分类方法。然而，大多数核方法的理论结果适用于估计，而不一定适用于分类。在本文中，我们证明了当估计两个密度之间的差异时，最优平滑参数是互补组样本大小的递增函数，并且我们提供了一个小的模拟研究，该研究检验了核密度方法在最终目标是分类时的相对性能。分类组合中一个相对较新的概念是“增强”，本文提出了一种增强核密度分类器的算法。我们注意到，增强与先前提出的核密度估计中的偏差减少方法密切相关，并表明它将如何在分类中享有类似的属性。我们表明，增强核分类器减少了偏差，同时只略微增加了方差，总体上减少了误差。数值例子和模拟用来说明研究结果，我们也提出了进一步的研究领域。
Kernel density estimation is a commonly used approach to classification. However, most of the theoretical results for kernel methods apply to estimationper seand not necessarily to classification. In this paper we show that when estimating the difference between two densities, the optimal smoothing parameters areincreasingfunctions 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.