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  • Soft computing on small data sets
    Novak, Bojan, računalničar
    The fusion of artificial neural networks (ANN) with soft computing enables to construct learning machines that are superior compared to classical ANN because knowledge can be extracted and explained ... in the form of simple rules. If the data sets are small it is hard to find the optimal structure of ANN because classical statistical laws do not apply. One possible remedy is the structural risk minimization method applied together with a VC dimension estimation technique. The construction of the optimal ANN structure is done in higher dimensional space. The distortion of an image in this transformationcan happen and the widely used expression for VC estimations based on minimal input data enclosing hypersphere and margin is not precise. An improvement ov VC dimension estimation is presented. It enables better actual error estimation and is particularly suitable for the small data sets. Tests on some real life data sets have confirmed the theoretical expectations.
    Vir: Informatica : an international journal of computing and informatics. - ISSN 0350-5596 (Vol. 25, no. 1, Apr. 2001, str. 83-88)
    Vrsta gradiva - članek, sestavni del
    Leto - 2001
    Jezik - angleški
    COBISS.SI-ID - 6268182

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    Digitalna knjižnica Slovenije - dLib.si

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