"A gold mine for researchers working on learning algorithms and
computer professionals who want to use them."
-- Mario Marchand, Physics Department, University of
Ottawa
Neural Network Learning and Expert Systems is the first
book to present a unified and in-depth development of neural network
learning algorithms and neural network expert systems. Especially
suitable for students and researchers in computer science,
engineering, and psychology, this text and reference provides a
systematic development of neural network learning algorithms from a
computational perspective, coupled with an extensive exploration of
neural network expert systems which shows how the power of neural
network learning can be harnessed to generate expert systems
automatically.
Features include a comprehensive treatment of the standard learning
algorithms (with many proofs), along with much original research on
algorithms and expert systems. Additional chapters explore
constructive algorithms, introduce computational learning theory, and
focus on expert system applications to noisy and redundant problems.
For students there is a large collection of exercises, as well as a
series of programming projects that lead to an extensive neural
network software package. All of the neural network models examined
can be implemented using standard programming languages on a
microcomputer.
Stephen l. Gallant taught courses in neural network learning and
expert systems as Associate Professor of Computer Science at
Northeastern University. He is currently a Senior Scientist at HNC,
Inc.
|
