Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

A summary is presented of the statistical mechanical theory of learning a rule with a neural
network, a rapidly advancing area which is closely related to other inverse problems …

Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …

A training algorithm that maximizes the margin between the training patterns and the
decision boundary is presented. The technique is applicable to a wide variety of the …

Spatial working memory, the caching of behaviourally relevant spatial cues on a timescale of
seconds, is a fundamental constituent of cognition. Although the prefrontal cortex and …

The typical fraction of the space of interactions between each pair of N Ising spins which
solve the problem of storing a given set of p random patterns as N-bit spin configurations is …

Neural Networks presents concepts of neural-network models and techniques of parallel
distributed processing in a three-step approach:-A brief overview of the neural structure of …

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 …

Exploring recent developments in the field, Coarse-Graining of Condensed Phase and
Biomolecular Systems examines systematic ways of constructing coarse-grained …

This book gives an introduction to the mathematical theory of cooperative behavior in active
systems of various origins, both natural and artificial. It is based on a lecture course in …