Conference paper
Pap-smear Classification Using Efficient Second Order Neural Network Training Algorithms
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.
Language: | English |
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Publisher: | Springer |
Year: | 2004 |
Pages: | 230-245 |
Proceedings: | 3rd Hellenic Conference on Artificial Intelligence |
Series: | Lecture Notes in Computer Science |
ISBN: | 1280307382 , 3540219374 , 3540246746 , 9781280307386 , 9783540219378 and 9783540246749 |
ISSN: | 03029743 |
Types: | Conference paper |
DOI: | 10.1007/978-3-540-24674-9_25 |