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摘要翻译：
近年来，核密度估计被计算机科学家用来建模机器学习问题。基于核密度估计的分类器构造方法具有较低的时间复杂度O(n)或O(n*log(n))，其中n是采样实例数。关于核密度估计器的设计，一个重要的问题是随着采样次数的增加，点均方误差(MSE)和/或积分均方误差(IMSE)减小的速度有多快。本文证明了只要感兴趣点的概率密度函数满足一定条件，无论向量空间的维数如何，利用所提出的核函数，密度估计的点态均方误差收敛于O(n^-2/3)是可行的。
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英文标题：
《Supervised Machine Learning with a Novel Kernel Density Estimator》
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作者：
Yen-Jen Oyang, Darby Tien-Hao Chang, Yu-Yen Ou, Hao-Geng Hung,
Chih-Peng Wu and Chien-Yu Chen
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
In recent years, kernel density estimation has been exploited by computer scientists to model machine learning problems. The kernel density estimation based approaches are of interest due to the low time complexity of either O(n) or O(n*log(n)) for constructing a classifier, where n is the number of sampling instances. Concerning design of kernel density estimators, one essential issue is how fast the pointwise mean square error (MSE) and/or the integrated mean square error (IMSE) diminish as the number of sampling instances increases. In this article, it is shown that with the proposed kernel function it is feasible to make the pointwise MSE of the density estimator converge at O(n^-2/3) regardless of the dimension of the vector space, provided that the probability density function at the point of interest meets certain conditions.
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PDF链接：
https://arxiv.org/pdf/709.276
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