Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments

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dc.creatorBoinee, P.
dc.creatorDe Angelis, A.
dc.creatorMilotti, E.
dc.date2003-07-12
dc.date2003-07-16
dc.date.accessioned2026-07-07T03:20:02Z
dc.date.available2026-07-07T03:20:02Z
dc.descriptionSelf-Organising Maps (SOMs) are effective tools in classification problems, and in recent years the even more powerful Dynamic Growing Neural Networks, a variant of SOMs, have been developed. Automatic Classification (also called clustering) is an important and difficult problem in many Astrophysical experiments, for instance, Gamma Ray Burst classification, or gamma-hadron separation. After a brief introduction to classification problem, we discuss Self-Organising Maps in section 2. Section 3 discusses with various models of growing neural networks and finally in section 4 we discuss the research perspectives in growing neural networks for efficient classification in astrophysical problems.
dc.description9 Pages, corrected authors name format
dc.identifierhttps://arxiv.org/abs/cs/0307031
dc.identifierhttp://arxiv.org/abs/cs/0307031
dc.identifierS. Ciprini, A. De Angelis, P. Lubrano and O. Mansutti (eds.): Proc. of ``Science with the New Generation of High Energy Gamma-ray Experiments'' (Perugia, Italy, May 2003). Forum, Udine 2003, p. 177
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31695
dc.subjectNeural and Evolutionary Computing
dc.subjectAstrophysics
dc.subjectArtificial Intelligence
dc.subjectI.5.1; I.5.3
dc.titleAutomatic Classification using Self-Organising Neural Networks in Astrophysical Experiments
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