Books > Computing & IT > Applications of computing > Artificial intelligence
|
Buy Now
Theoretical Advances in Neural Computation and Learning (Hardcover, 1994 ed.)
Loot Price: R4,610
Discovery Miles 46 100
|
|
Theoretical Advances in Neural Computation and Learning (Hardcover, 1994 ed.)
Expected to ship within 10 - 15 working days
|
Theoretical Advances in Neural Computation and Learning brings
together in one volume some of the recent advances in the
development of a theoretical framework for studying neural
networks. A variety of novel techniques from disciplines such as
computer science, electrical engineering, statistics, and
mathematics have been integrated and applied to develop
ground-breaking analytical tools for such studies. This volume
emphasizes the computational issues in artificial neural networks
and compiles a set of pioneering research works, which together
establish a general framework for studying the complexity of neural
networks and their learning capabilities. This book represents one
of the first efforts to highlight these fundamental results, and
provides a unified platform for a theoretical exploration of neural
computation. Each chapter is authored by a leading researcher
and/or scholar who has made significant contributions in this area.
Part 1 provides a complexity theoretic study of different models of
neural computation. Complexity measures for neural models are
introduced, and techniques for the efficient design of networks for
performing basic computations, as well as analytical tools for
understanding the capabilities and limitations of neural
computation are discussed. The results describe how the
computational cost of a neural network increases with the problem
size. Equally important, these results go beyond the study of
single neural elements, and establish to computational power of
multilayer networks. Part 2 discusses concepts and results
concerning learning using models of neural computation. Basic
concepts such as VC-dimension and PAC-learning are introduced, and
recentresults relating neural networks to learning theory are
derived. In addition, a number of the chapters address fundamental
issues concerning learning algorithms, such as accuracy and rate of
convergence, selection of training data, and efficient algorithms
for learning useful classes of mappings.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
You might also like..
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.