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 language | English |
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Title of host publication | Methods and Applications of Artificial Intelligence |
Publisher | Springer |
Publication date | 2004 |
Pages | 230-245 |
ISBN (Print) | 3-540-21937-4, 978-3-540-21937-8 |
ISBN (Electronic) | 978-3-540-24674-9 |
DOIs | |
Publication status | Published - 2004 |
Event | 3rd Hellenic Conference on Artificial Intelligence - Samos, Greece Duration: 5 May 2004 → 8 May 2004 Conference number: 3 |
Conference
Conference | 3rd Hellenic Conference on Artificial Intelligence |
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Number | 3 |
Country/Territory | Greece |
City | Samos |
Period | 05/05/2004 → 08/05/2004 |
Series | Lecture Notes in Computer Science |
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Volume | 3025 |
ISSN | 0302-9743 |