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Finding Gamma-Ray Burst Classes.

Gamma-ray bursts gamma-ray burst
A short-lived, localized, and intense burst of gamma radiation that originates outside the solar system from an unknown source.
 (GRBs) are the most energetic natural events in the universe. Traditional methods have long indicated that two distinct GRB GRB Gamma Ray Burst(er)
GRB Graduate Recruitment Bureau
GRB Grid Resource Broker
GRB Grootschalig Referentiebestand (Dutch: large scale mapping program)
GRB Gharb
 classes exist, while newer statistical clustering techniques have demonstrated the existence of a third GRB class. We show how pattern recognition algorithms The following is a list of the algorithms described in Wikipedia. See also the list of data structures, list of algorithm general topics and list of terms relating to algorithms and data structures.  from the artificial intelligence (AI) branch of computer science can identify new GRB classes. More importantly, we use AI algorithms to explain class behaviors. In other words Adv. 1. in other words - otherwise stated; "in other words, we are broke"
put differently
, AI algorithms can be used to determine whether classes are discrete source populations, or whether other effects (e.g. instrumental biases, sampling biases, data correlations, and/or intrinsic intrinsic /in·trin·sic/ (in-trin´sik) situated entirely within or pertaining exclusively to a part.

1. Of or relating to the essential nature of a thing.

 measurement errors) are responsible for producing class behaviors.
Jon Hakkila, Tim Giblin
Department of Physics and Astronomy
College of Charleston

William S. Paciesas
Department of Physics and Astronomy
University of Alabama in Huntsville

David J. Haglin, Richard J. Roiger
Department of Computer and Information Services
Minnesota State University, Mankato
COPYRIGHT 2001 South Carolina Academy of Science
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2001 Gale, Cengage Learning. All rights reserved.

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Title Annotation:using artificial intelligence algorithms
Author:Hakkila, Jon; Giblin, Tim; Paciesas, William S.; Haglin, David J.; Roiger, Richard J.
Publication:Bulletin of the South Carolina Academy of Science
Article Type:Brief Article
Geographic Code:1U5SC
Date:Jan 1, 2001
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