The paper examines the general classifier combination problem under strict separation of the classifier and combinator design. Several desirable combinator properties are identified: omnitype mixed type and correlated classifier combination, redundant classifier elimination, model complexity control, and dynamic selection combination. By adapting some of the theories and algorithms developed for neural network learning. They present a combination model which provides a solution to these problems. Experimental results on handwritten digits verify these findings.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A theory of classifier combination: the neural network approach


    Contributors:


    Publication date :

    1995-01-01


    Size :

    525622 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Theory of Classifier Combination: The Neural Network Approach

    Lee, D.-S. / Srihari, S. N. | British Library Conference Proceedings | 1995


    Dynamic classifier combination using neural network [2422-04]

    Lee, D.-S. / Srihari, S. N. / SPIE | British Library Conference Proceedings | 1995


    Spiking Neural Network E-Nose classifier chip

    Abdel-Aty-Zohdy, H S / Allen, J N / Ewing, R L | IEEE | 2010


    COMPOSITE MATERIALS' CONDITION CLASSIFIER BASED ON NEURAL NETWORK OF ADAPTIVE RESONANCE THEORY

    В. Єременко / П. Шегедін / А. Переїденко | DOAJ | 2012

    Free access

    Classifier Combination for Vehicle Silhouettes Recognition

    Prampero, P. S. / de Carvalho, A. C. P. L. F. / Institution of Electrical Engineers et al. | British Library Conference Proceedings | 1999