(1) Stat-Math Unit, Indian Statistical Institute, 203, B. T. Road, Calcutta 700108, India
Abstract:
Summary Regression and classification problems can be viewed as special cases of the problem of function estimation. It is rather
well known that a two-layer perceptron with sigmoidal transformation functions can approximate any continuous function on
the compact subsets ofRP if there are sufficient number of hidden nodes. In this paper, we present an algorithm for fitting perceptron models, which
is quite different from the usual backpropagation or Levenberg-Marquardt algorithm. This new algorithm based on backfitting
ensures a better convergence than backpropagation. We have also used resampling techniques to select an ideal number of hidden
nodes automatically using the training data itself. This resampling technique helps to avoid the problem of overfitting that
one faces for the usual perceptron learning algorithms without any model selection scheme. Case studies and simulation results
are presented to illustrate the performance of this proposed algorithm.