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.
A Bradford Book
About the Editor
Stephen José Hanson is Professor of Psychology (Newark Campus) and Member of the Cognitive Science Center (New Brunswick Campus) at Rutgers University.