标题： 比较K近邻算法与势能法在分类问题中的应用。 使用E.M. Mirkes的KNN小程序及真实基准数据集的案例研究 显示英文标题

标题： Comparing K-Nearest Neighbors and Potential Energy Method in classification problem. A case study using KNN applet by E.M. Mirkes and real life benchmark data sets

摘要： K-最近邻（KNN）方法被用于许多有监督学习的分类问题。 势能（PE）方法也基于其物理隐喻被开发用于分类问题。 实验中使用的势能包括 Yukawa 势和高斯势。 本文使用小应用程序和 MATLAB 程序以及真实生活基准数据来分析 KNN 和 PE 方法在分类问题中的性能。 结果显示，总体上，KNN 和 PE 方法的性能相似。 特别是，当数据库分布中数据密度较高时，Yukawa 势下的 PE 方法的性能比 KNN 差。 当应用高斯势时，PE 和 KNN 的结果表现出类似的行为。 所用的指标包括相关系数和信息增益。 显示英文摘要

摘要： K-nearest neighbors (KNN) method is used in many supervised learning classification problems. Potential Energy (PE) method is also developed for classification problems based on its physical metaphor. The energy potential used in the experiments are Yukawa potential and Gaussian Potential. In this paper, I use both applet and MATLAB program with real life benchmark data to analyze the performances of KNN and PE method in classification problems. The results show that in general, KNN and PE methods have similar performance. In particular, PE with Yukawa potential has worse performance than KNN when the density of the data is higher in the distribution of the database. When the Gaussian potential is applied, the results from PE and KNN have similar behavior. The indicators used are correlation coefficients and information gain.