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

Abstract

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.

Authors:
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Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
922310
Report Number(s):
UCRL-TR-227939
TRN: US200806%%223
DOE Contract Number:
W-7405-ENG-48
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; 42 ENGINEERING; CLASSIFICATION; DETECTION; METRICS; PERFORMANCE; PROGRESS REPORT; VECTORS

Citation Formats

Chen, B, Hickling, T, Krnjajic, M, Hanley, W, Clark, G, Nitao, J, Knapp, D, Hiller, L, and Mugge, M. Multi-Layer Perceptrons and Support Vector Machines for Detection Problems with Low False Alarm Requirements: an Eight-Month Progress Report. United States: N. p., 2007. Web. doi:10.2172/922310.
Chen, B, Hickling, T, Krnjajic, M, Hanley, W, Clark, G, Nitao, J, Knapp, D, Hiller, L, & Mugge, M. Multi-Layer Perceptrons and Support Vector Machines for Detection Problems with Low False Alarm Requirements: an Eight-Month Progress Report. United States. doi:10.2172/922310.
Chen, B, Hickling, T, Krnjajic, M, Hanley, W, Clark, G, Nitao, J, Knapp, D, Hiller, L, and Mugge, M. Tue . "Multi-Layer Perceptrons and Support Vector Machines for Detection Problems with Low False Alarm Requirements: an Eight-Month Progress Report". United States. doi:10.2172/922310. https://www.osti.gov/servlets/purl/922310.
@article{osti_922310,
title = {Multi-Layer Perceptrons and Support Vector Machines for Detection Problems with Low False Alarm Requirements: an Eight-Month Progress Report},
author = {Chen, B and Hickling, T and Krnjajic, M and Hanley, W and Clark, G and Nitao, J and Knapp, D and Hiller, L and Mugge, M},
abstractNote = {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.},
doi = {10.2172/922310},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 09 00:00:00 EST 2007},
month = {Tue Jan 09 00:00:00 EST 2007}
}

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