Analysis of neural networks through base functions

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    Abstract

    Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more as a mysterious "black box" [1]. This is an important aspect of the functionality of any technology, as users will be interested in "how it works" before trusting it completely. Although much research has already been done to "open the box," there is a notable hiatus in known publications on analysis of neural networks. So far, mainly sensitivity analysis and rule extraction methods have been used to analyze neural networks. However, these can only be applied in a limited subset of the problem domains where neural network solutions are encountered.
    Original languageUndefined
    Title of host publicationLearning Solutions
    Place of PublicationNetherlands
    PublisherSTW
    Pages34-35
    Number of pages2
    ISBN (Print)-
    Publication statusPublished - Jun 2002
    EventLerende Oplossingen: Lerende Oplossingen - Nijmegen, Netherlands
    Duration: 14 Jun 200214 Jun 2002

    Publication series

    Name
    PublisherTechnologiestichting STW 05 30-01

    Seminar

    SeminarLerende Oplossingen
    CountryNetherlands
    CityNijmegen
    Period14/06/0214/06/02

    Keywords

    • METIS-206438
    • IR-43371
    • EWI-1417

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