Pap-smear Classification Using Efficient Second Order Neural Network Training Algorithms

Nikolaos Ampazis, George Dounias, Jan Jantzen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    In this paper we make use of two highly efficient second order neural network training algorithms, namely the LMAM (Levenberg-Marquardt with Adaptive Momentum) and OLMAM (Optimized Levenberg-Marquardt with Adaptive Momentum), for the construction of an efficient pap-smear test classifier. The algorithms are methodologically similar, and are based on iterations of the form employed in the Levenberg-Marquardt (LM) method for non-linear least squares problems with the inclusion of an additional adaptive momentum term arising from the formulation of the training task as a constrained optimization problem. The classification results obtained from the application of the algorithms on a standard benchmark pap-smear data set reveal the power of the two methods to obtain excellent solutions in difficult classification problems whereas other standard computational intelligence techniques achieve inferior performances.
    Original languageEnglish
    Title of host publicationMethods and Applications of Artificial Intelligence
    PublisherSpringer
    Publication date2004
    Pages230-245
    ISBN (Print)3-540-21937-4, 978-3-540-21937-8
    ISBN (Electronic)978-3-540-24674-9
    DOIs
    Publication statusPublished - 2004
    Event3rd Hellenic Conference on Artificial Intelligence - Samos, Greece
    Duration: 5 May 20048 May 2004
    Conference number: 3

    Conference

    Conference3rd Hellenic Conference on Artificial Intelligence
    Number3
    Country/TerritoryGreece
    CitySamos
    Period05/05/200408/05/2004
    SeriesLecture Notes in Computer Science
    Volume3025
    ISSN0302-9743

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