Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments
<|reference_start|>Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments: 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.<|reference_end|>
@article{boinee2003automatic,
    title={Automatic Classification using Self-Organising Neural Networks in
  Astrophysical Experiments},
    author={P. Boinee, A. De Angelis, E. Milotti},
    journal={S. 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},
    year={2003},
    archivePrefix={arXiv},
    eprint={cs/0307031},
    primaryClass={cs.NE astro-ph cs.AI}
}