Edited
By William Ramsey, David E. Rumelhart, Stephen P. Stich
February 04, 2016
The philosophy of cognitive science has recently become one of the most exciting and fastest growing domains of philosophical inquiry and analysis. Until the early 1980s, nearly all of the models developed treated cognitive processes -- like problem solving, language comprehension, memory, and ...
Edited
By Paul Smolensky, Michael C. Mozer
May 07, 2015
Recent years have seen an explosion of new mathematical results on learning and processing in neural networks. This body of results rests on a breadth of mathematical background which even few specialists possess. In a format intermediate between a textbook and a collection of research articles, ...
Edited
By Yves Chauvin, David E. Rumelhart
February 01, 1995
Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation. The first section presents the theory and principles behind backpropagation as seen from different perspectives such as statistics, machine learning, and dynamical systems. The ...
Edited
By Mark A. Gluck, David E. Rumelhart
February 01, 1990
Written for cognitive scientists, psychologists, computer scientists, engineers, and neuroscientists, this book provides an accessible overview of how computational network models are being used to model neurobiological phenomena. Each chapter presents a representative example of how biological ...