This is the third in a series of edited volumes exploring the evolving
landscape of learning systems research which spans theory and
experiment, symbols and signals. It continues the exploration of the
synthesis of the machine learning subdisciplines begun in volumes I
and II. The nineteen contributions cover learning theory, empirical
comparisons of learning algorithms, the use of prior knowledge,
probabilistic concepts, and the effect of variations over time in the
concepts and feedback from the environment.
The goal of this series is to explore the intersection of three
historically distinct areas of learning research: computational
learning theory, neural networks and AI machine learning. Although
each field has its own conferences, journals, language, research,
results, and directions, there is a growing intersection and effort to
bring these fields into closer coordination.
Can the various communities learn anything from one another? These
volumes present research that should be of interest to practitioners
of the various subdisciplines of machine learning, addressing
questions that are of interest across the range of machine learning
approaches, comparing various approaches on specific problems and
expanding the theory to cover more realistic cases.