Theoretical Interpretations and Applications of Radial Basis Function Networks

Blanzieri, Enrico (2003) Theoretical Interpretations and Applications of Radial Basis Function Networks. UNSPECIFIED. (Unpublished)

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    Abstract

    Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains.

    Item Type: Departmental Technical Report
    Department or Research center: Information Engineering and Computer Science
    Subjects: Q Science > QA Mathematics > QA075 Electronic computers. Computer science
    Report Number: DIT-03-023
    Repository staff approval on: 30 May 2003

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