title: Weight configurations of trained perceptrons.
abstract: We strive to predict the function mapping and rules performed by a trained perceptron from studying the weights. We derive a few properties of the trained weights and show how the perceptron's representation of knowledge, rules and functions depend on these properties. Two types of perceptrons are studied--one case with continuous inputs and one hidden layer, the other a simple binary classifier with boolean inputs and no hidden units.
authors.affiliation: Department of Medical Physics and Biophysics, University of Nijmegen, The Netherlands.
authors.forename: H S
authors.initials: HS
authors.lastname: Toh
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer