Stochastic Learning Dynamics and Non-Linear Dimension Reduction

摘要

This reports cover research activity under the grant in place from April 1993through March 1994. Our research addressed algorithms and theory for stochastic learning, non-linear extensions of principal component analysis (PCA) for dimension-reduction, network pruning and methods to incorporate desired invariances into learning. (AN).