Multi-Layer Perceptrons and Support Vector Machines for Detection Problems with Low False Alarm Requirements: an Eight-Month Progress Report

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In this project, the basic problem is to automatically separate test samples into one of two categories: clean or corrupt. This type of classification problem is known as a two-class classification problem or detection problem. In what follows, we refer to clean examples as negative examples and corrupt examples as positive examples. In a detection problem, a classifier decision on any one sample can be grouped into one of four decision categories: true negative, true positive, false negative and false positive. These four categories are illustrated by Table 1. True negatives and true positives are cases where the classifier has ... continued below

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PDF-file: 64 pages; size: 0.6 Mbytes

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Chen, B; Hickling, T; Krnjajic, M; Hanley, W; Clark, G; Nitao, J et al. January 9, 2007.

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Description

In this project, the basic problem is to automatically separate test samples into one of two categories: clean or corrupt. This type of classification problem is known as a two-class classification problem or detection problem. In what follows, we refer to clean examples as negative examples and corrupt examples as positive examples. In a detection problem, a classifier decision on any one sample can be grouped into one of four decision categories: true negative, true positive, false negative and false positive. These four categories are illustrated by Table 1. True negatives and true positives are cases where the classifier has made the correct decision. False positives are cases where the classifier decides positive when the true nature of the sample was negative, and false negatives are cases where the classifier decides negative when the sample was actually positive. To evaluate the performance of a classifier, we run the classifier on all the samples of a data set and then count all the instances of true negatives, true positives, false negatives, and false positives. All of the performance metrics in this report are then formed from a combination of these four basic decision categories.

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PDF-file: 64 pages; size: 0.6 Mbytes

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  • Report No.: UCRL-TR-227939
  • Grant Number: W-7405-ENG-48
  • DOI: 10.2172/922310 | External Link
  • Office of Scientific & Technical Information Report Number: 922310
  • Archival Resource Key: ark:/67531/metadc901047

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Office of Scientific & Technical Information Technical Reports

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  • January 9, 2007

Added to The UNT Digital Library

  • Sept. 27, 2016, 1:39 a.m.

Description Last Updated

  • Dec. 6, 2016, 1:43 p.m.

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Chen, B; Hickling, T; Krnjajic, M; Hanley, W; Clark, G; Nitao, J et al. Multi-Layer Perceptrons and Support Vector Machines for Detection Problems with Low False Alarm Requirements: an Eight-Month Progress Report, report, January 9, 2007; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc901047/: accessed October 20, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.