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 (OptimizedLevenberg-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.