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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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基于PolSAR数据的k-最近邻居的机器学习分类.

Jodavid A Ferreira1,2, Anny K G Rodrigues1,3, Raydonal Ospina1,4

  • 1Universidade Federal de Pernambuco, Departamento de Estatística, CASTLab, Av. Jornalista Anibal Fernandes, s/n, Cidade Universitária, 50740-540 Recife, PE, Brazil.

Anais da Academia Brasileira de Ciencias
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概括
此摘要是机器生成的。

对于极度测量合成孔径雷达 (PolSAR) 图像的机器学习分类显示出有希望的结果. 适应的方法提供了良好的性能与复杂性相比,Kullback-Leibler距离优于K-近邻和支持向量机器.

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科学领域:

  • 遥感 遥感 遥感 遥感
  • 机器学习 机器学习
  • 图像处理 图像处理

背景情况:

  • 极度测量合成孔径雷达 (PolSAR) 图像对自动化分析提出了独特的挑战.
  • 标准的机器学习分类器经常在PolSAR数据中与固有的斑点和复杂的分散机制作斗争.

研究的目的:

  • 评估用于PolSAR图像分类的众所周知的机器学习技术的性能.
  • 评估不同方法的分类性能和计算复杂性之间的权衡.
  • 确定适合机器学习方法用于PolSAR数据分析.

主要方法:

  • 对比K-最近邻居 (KNN),支持向量机 (SVM),随机决策树和基于距离的随机分类器 (Kullback-Leibler).
  • 这些分类器在真实PolSAR数据集上的应用和测试.
  • 对分类准确度和计算效率的分析.

主要成果:

  • K-Nearest Neighbors (KNN) 和支持矢量机 (SVM) 的表现不佳,可能是因为它们无法处理PolSAR斑点和地形特性.
  • 经过调整的标准机器学习方法显示了性能与复杂度的良好比率.
  • 与KNN和SVM相比,库尔巴克-莱布勒随机距离方法显示出更好的结果.

结论:

  • 标准的机器学习技术,当适当地适应时,可以在PolSAR图像分类中实现出色的性能-复杂性权衡.
  • 库尔巴克-莱布勒随机距离方法是PolSAR图像分类的一个有希望的方法.
  • 进一步研究适应机器学习用于PolSAR数据是有必要的.