Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments

P. Boinee; A. De Angelis; E. Milotti

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arXiv · cs/0307031

Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments

P. Boinee·A. De Angelis·E. Milotti

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Abstract

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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cs.NE astro-ph cs.AI

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P. Boinee, A. De Angelis, E. Milotti. 2003-07-16. Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments. https://arxiv.org/abs/cs/0307031

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