Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Haglin, David J."'
Autor:
Hakkila, Jon, Giblin, Timothy W., Roiger, Richard J., Haglin, David J., Paciesas, William S., Meegan, Charles A.
Publikováno v:
Astrophys.J. 582 (2003) 320-329
Unsupervised pattern recognition algorithms support the existence of three gamma-ray burst classes; Class I (long, large fluence bursts of intermediate spectral hardness), Class II (short, small fluence, hard bursts), and Class III (soft bursts of in
Externí odkaz:
http://arxiv.org/abs/astro-ph/0209073
Autor:
Hakkila, Jon, Roiger, Richard J., Haglin, David J., Mallozzi, Robert S., Pendleton, Geoffrey N., Meegan, Charles A.
Gamma-ray bursts provide what is probably one of the messiest of all astrophysical data sets. Burst class properties are indistinct, as overlapping characteristics of individual bursts are convolved with effects of instrumental and sampling biases. D
Externí odkaz:
http://arxiv.org/abs/astro-ph/0011583
We describe the design of a suite of software tools to allow users to query Gamma-Ray Burst (GRB) data and perform data mining expeditions. We call this suite of tools a shed (SHell for Expeditions using Datamining). Our schedule is to have a complet
Externí odkaz:
http://arxiv.org/abs/astro-ph/0001431
Autor:
Roiger, Richard J., Hakkila, Jon, Haglin, David J., Pendleton, Geoffrey N., Mallozzi, Robert S.
We use ESX, a product of Information Acumen Corporation, to perform unsupervised learning on a data set containing 797 gamma-ray bursts taken from the BATSE 3B catalog. Assuming all attributes to be distributed logNormally, Mukherjee et al. (1998) an
Externí odkaz:
http://arxiv.org/abs/astro-ph/0001381
Autor:
Hakkila, Jon, Meegan, Charles A., Pendleton, Geoffrey N., Mallozzi, Robert S., Haglin, David J., Roiger, Richard J.
The fluence duration bias causes fluences and durations of faint gamma-ray bursts to be systematically underestimated relative to their peak fluxes. Using Monte Carlo analysis, we demonstrate how this effect explains characteristics of structure of t
Externí odkaz:
http://arxiv.org/abs/astro-ph/0001338
Autor:
Hakkila, Jon, Haglin, David J., Roiger, Richard J., Mallozzi, Robert S., Pendleton, Geoffrey N., Meegan, Charles A.
The three gamma-ray burst (GRB) classes identified by statistical clustering analysis (Mukherjee et al. 1998) are examined using the pattern recognition algorithm C4.5 (Quinlan 1986). Although the statistical existence of Class 3 (intermediate durati
Externí odkaz:
http://arxiv.org/abs/astro-ph/0001335
Autor:
Hakkila, Jon, Haglin, David J., Roiger, Richard J., Mallozzi, Robert S., Pendleton, Geoffrey N., Meegan, Charles A.
Artificial intelligence (AI) classifiers can be used to classify unknowns, refine existing classification parameters, and identify/screen out ineffectual parameters. We present an AI methodology for classifying new gamma-ray bursts, along with some p
Externí odkaz:
http://arxiv.org/abs/astro-ph/9712077
Publikováno v:
Emerging Trends in Knowledge Discovery & Data Mining; 2013, p141-156, 16p
Autor:
Haglin, David J., Holder, Lawrence B.
Publikováno v:
2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops & Phd Forum; 2013, p1899-1904, 6p
Publikováno v:
2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops & Phd Forum; 2013, p1656-1664, 9p