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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