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Author: Eva Volna

Affiliation: University of Ostrava, Czech Republic

Abstract: The majority of this paper relies on some forms of automatic decomposition tasks into modules. Both described methods execute automatic neural network modularization. Modules in neural networks emerge; we do not build them straightforward by penalizing interference between modules. The concept of emergence takes an important role in the study of the design of neural networks. In the paper, we study an emergence of modular connectionist architecture of neural networks, in which networks composing the architecture compete to learn the training patterns directly from the interaction of reproduction with the task environment. Network architectures emerge from an initial set of randomly connected networks. In this way can be eliminated connections so as to dedicate different portions of the system to learn different tasks. Mentioned methods were demonstrated for experimental task solving.

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Paper citation in several formats:
Volna, E. (2010). Automatic Modularization of Artificial Neural Networks . In Proceedings of the 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2010) - Workshop ANNIIP; ISBN 978-989-8425-03-4, SciTePress, pages 23-32. DOI: 10.5220/0003023800230032

@conference{workshop anniip10,
author={Eva Volna.},
title={Automatic Modularization of Artificial Neural Networks },
booktitle={Proceedings of the 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2010) - Workshop ANNIIP},
year={2010},
pages={23-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003023800230032},
isbn={978-989-8425-03-4},
}

TY - CONF

JO - Proceedings of the 6th International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2010) - Workshop ANNIIP
TI - Automatic Modularization of Artificial Neural Networks
SN - 978-989-8425-03-4
AU - Volna, E.
PY - 2010
SP - 23
EP - 32
DO - 10.5220/0003023800230032
PB - SciTePress