Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks. He rigorously exposes the computational difficulties in training neural networks and explores how certain design principles will or will not make the problems easier.
Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first...
Neural Networks for Pattern Recognition takes the pioneering work in artificial neural networks by Stephen Grossberg and his colleagues to a new level. In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Following a tutorial of existing neural...
In this book, Shimon Ullman focuses on the processes of high-level vision that deal with the interpretation and use of what is seen in the image. In particular, he examines two major problems. The first, object recognition and classification, involves recognizing objects despite large variations in appearance caused by changes in viewing...
Perhaps the major obstacle to the development of computer programs
capable of the sophisticated processing of natural language is the
problem of representing and using the large and varied quantities of
world or domain knowledge that are, in general, required. This book
describes an attempt to circumvent this obstacle for one...
The papers in this volume are the refereed papers presented at AI-2010, the Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2010 in both the technical and the application streams. They present new and innovative developments and applications, divided...
Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas.
Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each...
The video game industry has grown explosively over the past decade
and is now a major provider of home entertainment. Since the North
American release of the Nintendo Entertainment System (NES) in
1985, game industry revenues have also grown substantially and, according
to the marketing research company NDP Group, grossed...
For this book, the editors invited contributions from indispensable research areas relevant to "chance discovery", which has been defined as the discovery of events significant for making a decision, and studied since 2000. The chapters contain contributions to identifying rare or hidden events and explaining their significance. The...
Approaches to building machines that can learn from experience abound - from connectionist learning algorithms and genetic algorithms to statistical mechanics and a learning system based on Piaget's theories of early childhood development. This monograph describes results derived from the mathematically oriented framework of computational...
Cay Horstmann's Python for Everyone provides readers with step-by-step guidance, a feature that is immensely helpful for building confidence and providing an outline for the task at hand. “Problem Solving” sections stress the importance of design and planning while “How...
What matters in understanding digital media? Is looking at the external appearance and audience experience of software enough--or should we look further? In Expressive Processing, Noah Wardrip-Fruin argues that understanding what goes on beneath the surface, the computational processes that make digital media function, is...
