Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments

Details Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments

2003-07-12

arxiv.org/abs/cs/0307031v2

S. Ciprini, A. De Angelis, P. Lubrano and O. Mansutti (eds.): Proc. of ``Science with the New Generation of High Energy Gamma-

Computer Science

Neural and Evolutionary Computing

9 Pages, corrected authors name format

Scientific paper

Self-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.

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