Critical Vector Learning to Construct RBF Classifiers

Daming Shi, G.S. Ng, Junbin Gao, D.S. Yeung

    Research output: Book chapter/Published conference paperConference paperpeer-review

    1 Citation (Scopus)
    7 Downloads (Pure)


    Sensitivity is initially investigated for the construction of a network prior to its design. Sensitivity analysis applied to network pruning seems particularly useful and valuable when network training involves a large amount of redundant data. This paper proposes a novel learning algorithm for the construction of radial basis function (RBF) classifiers using sensitive vectors (SenV), to which the output is the most sensitive. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of the output's sensitivity to the training data. In classification, the minimal number of such hidden neurons with the maximal sensitivity will be the most generalizable to unknown data. Our experimental results suggests that our proposed methodology outperforms classical RBF classifiers constructed by clustering.
    Original languageEnglish
    Title of host publication17th International Conference on Pattern Recognition (ICPR'04)
    Place of PublicationUSA
    Number of pages4
    ISBN (Electronic)0769521282
    Publication statusPublished - 2004
    EventInternational Conference on Pattern Recognition - Cambridge UK, United Kingdom
    Duration: 23 Aug 200426 Aug 2004


    ConferenceInternational Conference on Pattern Recognition
    Country/TerritoryUnited Kingdom


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