1991
DOI: 10.1021/ac00009a021
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Development and optimization of piecewise linear discriminants for the automated detection of chemical species

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Cited by 39 publications
(35 citation statements)
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“…In the work described here, the patterns in the active data class are generated synthetically from the modulation of a single laboratory-collected spectrum. [44][45][46][47][48] These spectra are then overlaid with backgrounds collected from a broad range of locations around the nation, in order to simulate any theoretical scenario the ASPECT program may encounter from high to low intensity sources. This collection of synthetic active spectra is supplemented by a second set of inactive spectra drawn from a separate pool of backgrounds collected from multiple vetted locations.…”
Section: Referencesmentioning
confidence: 99%
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“…In the work described here, the patterns in the active data class are generated synthetically from the modulation of a single laboratory-collected spectrum. [44][45][46][47][48] These spectra are then overlaid with backgrounds collected from a broad range of locations around the nation, in order to simulate any theoretical scenario the ASPECT program may encounter from high to low intensity sources. This collection of synthetic active spectra is supplemented by a second set of inactive spectra drawn from a separate pool of backgrounds collected from multiple vetted locations.…”
Section: Referencesmentioning
confidence: 99%
“…Recall that this method will attempt to generate a discriminant somewhere between the two classes, making the assumption that the data belong to a multivariate normal distribution. 45 This initialization step is making the assumption that the data follow a Gaussian distribution, but this is not likely the case. Therefore, this initial Bayesian step is merely designed to place an initial discriminant somewhere approximating the border region between the inactive and active classes.…”
Section: Initialization and Translationmentioning
confidence: 99%
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