title: Contrast enhancement for backpropagation.
abstract: This paper analyzes the effect of data-contrast to a backpropagation (BP) network and introduces a data preprocessing algorithm that can improve the efficiency of the standard BP learning. The basic idea is to transform input data to a range that associates the high-slope region of the sigmoid function where a relatively large modification of weights occurs. A simple uniform transformation to such a desired range, however, can lead to a slow and unbalanced learning if the data distribution is heavily skewed. To facilitate data processing on such distribution, the authors propose a modified histogram equalization technique which enhances the sparing between the data points in the heavily concentrated regions of the distribution.
authors.affiliation: Dept. of Electr. and Comput. Eng., Minnesota Univ., Duluth, MN.
authors.forename: T M
authors.initials: TM
authors.lastname: Kwon
authors.forename: H
authors.initials: H
authors.lastname: Cheng