Performance comparison of second order conjugate algorithms in neural networks for predictive data mining

Abstract

In this paper, a performance comparison of several variations of the nonlinear conjugate gradient method has been investigated. Neural Networkbased prediction models for life insurance sector have been developed and their training has been done with a variety of first and second order algorithms to find an efficient training algorithm, but keeping the focus on conjugate gradient based methods. Traditional second order methods require computation of second order derivatives and need to compute hessian for quadratic termination; which is a tedious and memory consuming task. Here we employ conjugate gradient methods which bypass the computation of hessian, but still achieve quadratic termination and thus prove to be memory efficient.