The backpropagation learning method has opened a way to wide applications of neural
network research. It is a type of the stochastic descent method known in the sixties. The …

Pattern recognition has long been studied in relation to many different (and mainly
unrelated) applications, such as remote sensing, computer vision, space research, and …

This paper describes a data organization system and genuine content-addressable memory
called the WEBSOM. It is a two-layer self-organizing map (SOM) architecture where …

When a parameter space has a certain underlying structure, the ordinary gradient of a
function does not represent its steepest direction, but the natural gradient does. Information …

Physical neural networks are artificial neural networks that mimic synapses and neurons
using physical systems or materials. These networks harness the distinctive characteristics …

Learning is one of the things that humans do naturally, and it has always been a challenge
for us to understand the process. Nowadays this challenge has another dimension as we try …

A statistical theory for overtraining is proposed. The analysis treats general realizable
stochastic neural networks, trained with Kullback-Leibler divergence in the asymptotic case …

The problem of on-line learning in two-layer neural networks is studied within the framework
of statistical mechanics. A fully connected committee machine with K hidden units is trained …

Abstract The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount
of interest among researches and practitioners in a wide variety of elds. The SOM and a …

The parameter space of neural networks has a Riemannian met (cid: 173) ric structure. The
natural Riemannian gradient should be used instead of the conventional gradient, since the …