A Study of the Radial Basis Function Neural Network Classifiers using Known Data of Varying Accuracy and Complexity

Patricia Crowther, Robert Cox, Dharmendra Sharma

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    Abstract

    Neural networks are increasingly used in a wide variety of applications such as speech recognition, diagnostic prediction, income prediction and credit screening. This paper empirically compares the performance of Radial Basis Function (RBF) and Multilayer Perceptron (MLP) neural networks using artificially generated data sets, enabling us to accurately chart the effectiveness of each network type and to provide some guidance to practitioners as to which type of network to use with their data. We find that when the discriminator is simple, RBF and MLP network performances are similar; when the number of data points is relatively small the MLP outperforms the RBF; when the discriminator is complex the RBF outperforms the MLP; and when the data has an unrelated input and the underlying discriminator is simple, the MLP outperforms the RBF
    Original languageEnglish
    Title of host publicationInternational Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES 2004
    Subtitle of host publicationKnowledge-Based Intelligent Information and Engineering Systems
    EditorsMircea Gh Negoita, Robert J Howlett, Lakhmi C Jain
    Place of PublicationBerlin, Germany
    PublisherSpringer
    Pages210-216
    Number of pages7
    ISBN (Print)9783540232056
    DOIs
    Publication statusPublished - 2004
    EventKnowledge-Based Intelligent Information and Engineering Systems - Wellington, New Zealand
    Duration: 22 Sep 200424 Sep 2004

    Conference

    ConferenceKnowledge-Based Intelligent Information and Engineering Systems
    CountryNew Zealand
    CityWellington
    Period22/09/0424/09/04

    Fingerprint

    Multilayer neural networks
    Classifiers
    Discriminators
    Neural networks
    Network performance
    Speech recognition
    Screening

    Cite this

    Crowther, P., Cox, R., & Sharma, D. (2004). A Study of the Radial Basis Function Neural Network Classifiers using Known Data of Varying Accuracy and Complexity. In M. G. Negoita, R. J. Howlett, & L. C. Jain (Eds.), International Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES 2004: Knowledge-Based Intelligent Information and Engineering Systems (pp. 210-216). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-540-30134-9_30
    Crowther, Patricia ; Cox, Robert ; Sharma, Dharmendra. / A Study of the Radial Basis Function Neural Network Classifiers using Known Data of Varying Accuracy and Complexity. International Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES 2004: Knowledge-Based Intelligent Information and Engineering Systems. editor / Mircea Gh Negoita ; Robert J Howlett ; Lakhmi C Jain. Berlin, Germany : Springer, 2004. pp. 210-216
    @inproceedings{8321d58fea684580ac713b9d851bb956,
    title = "A Study of the Radial Basis Function Neural Network Classifiers using Known Data of Varying Accuracy and Complexity",
    abstract = "Neural networks are increasingly used in a wide variety of applications such as speech recognition, diagnostic prediction, income prediction and credit screening. This paper empirically compares the performance of Radial Basis Function (RBF) and Multilayer Perceptron (MLP) neural networks using artificially generated data sets, enabling us to accurately chart the effectiveness of each network type and to provide some guidance to practitioners as to which type of network to use with their data. We find that when the discriminator is simple, RBF and MLP network performances are similar; when the number of data points is relatively small the MLP outperforms the RBF; when the discriminator is complex the RBF outperforms the MLP; and when the data has an unrelated input and the underlying discriminator is simple, the MLP outperforms the RBF",
    author = "Patricia Crowther and Robert Cox and Dharmendra Sharma",
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    Crowther, P, Cox, R & Sharma, D 2004, A Study of the Radial Basis Function Neural Network Classifiers using Known Data of Varying Accuracy and Complexity. in MG Negoita, RJ Howlett & LC Jain (eds), International Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES 2004: Knowledge-Based Intelligent Information and Engineering Systems. Springer, Berlin, Germany, pp. 210-216, Knowledge-Based Intelligent Information and Engineering Systems, Wellington, New Zealand, 22/09/04. https://doi.org/10.1007/978-3-540-30134-9_30

    A Study of the Radial Basis Function Neural Network Classifiers using Known Data of Varying Accuracy and Complexity. / Crowther, Patricia; Cox, Robert; Sharma, Dharmendra.

    International Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES 2004: Knowledge-Based Intelligent Information and Engineering Systems. ed. / Mircea Gh Negoita; Robert J Howlett; Lakhmi C Jain. Berlin, Germany : Springer, 2004. p. 210-216.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    TY - GEN

    T1 - A Study of the Radial Basis Function Neural Network Classifiers using Known Data of Varying Accuracy and Complexity

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    AU - Cox, Robert

    AU - Sharma, Dharmendra

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    N2 - Neural networks are increasingly used in a wide variety of applications such as speech recognition, diagnostic prediction, income prediction and credit screening. This paper empirically compares the performance of Radial Basis Function (RBF) and Multilayer Perceptron (MLP) neural networks using artificially generated data sets, enabling us to accurately chart the effectiveness of each network type and to provide some guidance to practitioners as to which type of network to use with their data. We find that when the discriminator is simple, RBF and MLP network performances are similar; when the number of data points is relatively small the MLP outperforms the RBF; when the discriminator is complex the RBF outperforms the MLP; and when the data has an unrelated input and the underlying discriminator is simple, the MLP outperforms the RBF

    AB - Neural networks are increasingly used in a wide variety of applications such as speech recognition, diagnostic prediction, income prediction and credit screening. This paper empirically compares the performance of Radial Basis Function (RBF) and Multilayer Perceptron (MLP) neural networks using artificially generated data sets, enabling us to accurately chart the effectiveness of each network type and to provide some guidance to practitioners as to which type of network to use with their data. We find that when the discriminator is simple, RBF and MLP network performances are similar; when the number of data points is relatively small the MLP outperforms the RBF; when the discriminator is complex the RBF outperforms the MLP; and when the data has an unrelated input and the underlying discriminator is simple, the MLP outperforms the RBF

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    BT - International Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES 2004

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    Crowther P, Cox R, Sharma D. A Study of the Radial Basis Function Neural Network Classifiers using Known Data of Varying Accuracy and Complexity. In Negoita MG, Howlett RJ, Jain LC, editors, International Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES 2004: Knowledge-Based Intelligent Information and Engineering Systems. Berlin, Germany: Springer. 2004. p. 210-216 https://doi.org/10.1007/978-3-540-30134-9_30