Transductive and Matched-Pair Machine Learning for Difficult Target Detection Problems

机译：用于困难目标检测问题的传导配对匹配机器学习

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This paper will describe the application of two non-traditional kinds of machine learning (transductive machine learning and the more recently proposed matched-pair machine learning) to the target detection problem. The approach combines explicit domain knowledge to model the target signal with a more agnostic machine-learning approach to characterize the background. The concept is illustrated with simulated data from an elliptically-contoured background distribution, on which a subpixel target of known spectral signature but unknown spatial extent has been implanted.

机译：本文将描述两种非传统类型的机器学习（转导式机器学习和最近提出的匹配对机器学习）在目标检测问题中的应用。该方法结合了明确的领域知识以对目标信号建模，并使用了更不可知的机器学习方法来表征背景。该概念用来自椭圆轮廓背景分布的模拟数据进行了说明，在该背景分布上已植入了已知光谱特征但空间范围未知的子像素目标。

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来源
《Algorithms and technologies for multispectral, hyperspectral, and ultraspectral imagery XX》|2014年|90880E.1-90880E.9|共9页
会议地点 Baltimore MD(US)
作者
James Theiler;
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作者单位

Los Alamos National Laboratory, Los Alamos, NM 87545;

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原文格式 PDF
正文语种 eng
中图分类
关键词
machine learning; target detection; transductive; matched-pair;

机译：机器学习目标检测；转导配对;

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Transductive and Matched-Pair Machine Learning for Difficult Target Detection Problems

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