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Comparative Bibliography of Ontogenic Neural Networks

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ICANN ’94 (ICANN 1994)

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Abstract

One of the most powerful aspects of neural networks is their ability to adapt to problems by changing their interconnection strengths according to a predetermined learning rule. On the other hand, one of the main drawbacks of neural networks is the lack of knowledge for determining the topology of the network, that is, the number of layers and number of neurons per layer. A special class of neural networks tries to overcome this problem by letting the network also automatically adapt its topology to the problem. These are the so called ontogenic neural networks. Other potential advantages of ontogenic neural networks are improved generalization, implementation optimization (size and/or execution speed), and the avoidance of local minima.

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Author information

Authors and Affiliations

  1. IDIAP, Case postale 609, CH-1920, Martigny, Switzerland

    E. Fiesler

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  1. E. Fiesler
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Editor information

Editors and Affiliations

  1. Dipartimento di Fisica Teorica e S.M.S.A, University of Salerno, Via S. Allende, 84081, Barnossi, Salerno, Italy

    Maria Marinaro

  2. Dipartimento di Informatica,Sistemistica,Telematica, University of Genova, Via Opera Pia 11A, 16145, Genova, Italy

    Pietro G. Morasso

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© 1994 Springer-Verlag London Limited

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Fiesler, E. (1994). Comparative Bibliography of Ontogenic Neural Networks. In: Marinaro, M., Morasso, P.G. (eds) ICANN ’94. ICANN 1994. Springer, London. https://doi.org/10.1007/978-1-4471-2097-1_188

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  • DOI: https://doi.org/10.1007/978-1-4471-2097-1_188

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  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19887-1

  • Online ISBN: 978-1-4471-2097-1

  • eBook Packages: Springer Book Archive

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