language: en

Metamorphosis Networks: An Alternative to Constructive Methods

Brian V. Bonnlander Michael C. Mozer
Department of Computer Science & Institute of Cognitive Science University of Colorado
Boulder, CO 80309-0430

Abstract Given a set of training examples, determining the appropriate number of free parameters is a challenging problem. Constructive learning algorithms attempt to solve this problem automatically by adding hidden units, and therefore free parameters, during learning. We explore an alternative class of algorithms|called metamorphosis algorithms|in which the number of units is fixed, but the number of free parameters gradually increases during learning. The architecture we investigate is composed of RBF units on a lattice, which imposes flexible constraints on the parameters of the network. Virtues of this approach include variable subset selection, robust parameter selection, multiresolution processing, and interpolation of sparse training data.

1 INTRODUCTION