title: An equalized error backpropagation algorithm for the on-line training of multilayer perceptrons.
abstract: The error backpropagation (EBP) training of a multilayer perceptron (MLP) may require a very large number of training epochs. Although the training time can usually be reduced considerably by adopting an on-line training paradigm, it can still be excessive when large networks have to be trained on lots of data. In this paper, a new on-line training algorithm is presented. It is called equalized EBP (EEBP), and it offers improved accuracy, speed, and robustness against badly scaled inputs. A major characteristic of EEBP is its utilization of weight specific learning rates whose relative magnitudes are derived from a priori computable properties of the network and the training data.
authors.affiliation: Electron. and Inf. Syst., Ghent Univ., Gent.
authors.forename: J P
authors.initials: JP
authors.lastname: Martens
authors.forename: N
authors.initials: N
authors.lastname: Weymaere