摘要翻译：
提出了一种基于密度估计的有监督机器学习算法。该算法具有O(n)个生成分类器的时间复杂度，其中n是训练数据集中的采样实例数。在涉及大型且仍在增长的数据库的当代应用程序中，该特性是非常理想的。与基于核密度估计的方法相比，该算法背后的数学基础不是基于训练实例数接近无穷大的假设。因此，在某些情况下，用该算法生成的分类器可以提供比基于核密度估计的分类器更高的预测精度。
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英文标题：
《Supervised Machine Learning with a Novel Pointwise Density Estimator》
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作者：
Yen-Jen Oyang, Chien-Yu Chen, Darby Tien-Hao Chang, and Chih-Peng Wu
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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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一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  This article proposes a novel density estimation based algorithm for carrying out supervised machine learning. The proposed algorithm features O(n) time complexity for generating a classifier, where n is the number of sampling instances in the training dataset. This feature is highly desirable in contemporary applications that involve large and still growing databases. In comparison with the kernel density estimation based approaches, the mathe-matical fundamental behind the proposed algorithm is not based on the assump-tion that the number of training instances approaches infinite. As a result, a classifier generated with the proposed algorithm may deliver higher prediction accuracy than the kernel density estimation based classifier in some cases.
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PDF链接：
https://arxiv.org/pdf/710.5896