Learning in linear neural networks: a survey
Baldi, P.F.; Hornik, K.
Neural Networks, IEEE Transactions on
Volume 6, Issue 4, Jul 1995 Page(s):837 - 858
Digital Object Identifier 10.1109/72.392248
Summary:Networks of linear units are the simplest kind of networks, where
the basic questions related to learning, generalization, and
self-organization can sometimes be answered analytically. We survey most
of the known results on linear networks, including: 1) backpropagation
learning and the structure of the error function landscape, 2) the
temporal evolution of generalization, and 3) unsupervised learning
algorithms and their properties. The connections to classical
statistical ideas, such as principal component analysis (PCA), are
emphasized as well as several simple but challenging open questions. A
few new results are also spread across the paper, including an analysis
of the effect of noise on backpropagation networks and a unified view of
all unsupervised algorithms
View citation and abstract |
Cart
