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arxiv cs/0307031 v2 pith:ENAM6USK submitted 2003-07-12 cs.NE astro-phcs.AI

Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments

classification cs.NE astro-phcs.AI
keywords classificationnetworksneuralastrophysicalgrowingsectionself-organisingautomatic
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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