79 citations · 79 across the 2 of their papers we have counts for
6 papers
How Sample Completeness Affects Gamma-Ray Burst Classification
Jon Hakkila, Timothy W. Giblin, Richard J. Roiger +3
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…
Mining Gamma-Ray Burst Data
Jon Hakkila, Richard J. Roiger, David J. Haglin +3
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 bu…
A GRB Tool Shed
David J. Haglin, Richard J. Roiger, Jon Hakkila +2
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 (SHel…
Unsupervised Induction and Gamma-Ray Burst Classification
Richard J. Roiger, Jon Hakkila, David J. Haglin +2
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 al…
Properties of Gamma-Ray Burst Classes
Jon Hakkila, David J. Haglin, Richard J. Roiger +3
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 198…
AI Gamma-Ray Burst Classification: Methodology/Preliminary Results
Jon Hakkila, David J. Haglin, Richard J. Roiger +3
Artificial intelligence (AI) classifiers can be used to classify unknowns, refine existing classification parameters, and identify/screen out ineffectual parameters. We present an…