Circuit complexity and neural networks
 Responsibility
 Ian Parberry.
 Imprint
 Cambridge, Mass. : MIT Press, ©1994.
 Physical description
 1 online resource (xxix, 270 pages) : illustrations
 Series
 Foundations of computing.
Online
More options
Description
Creators/Contributors
 Author/Creator
 Parberry, Ian.
Contents/Summary
 Bibliography
 Includes bibliographical references (pages 251257) and index.
 Contents

 Computers and computation
 the discrete neuron
 the Boolean neuron
 alternating circuits
 small, shallow alternating circuits
 threshold circuits
 cyclic networks
 probabilistic neural networks.
 (source: Nielsen Book Data)
 Publisher's summary

Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale  that is, how fast the computation time and number of neurons grow as the problem size increases. It surveys recent research in circuit complexity (a robust branch of theoretical computer science) and applies this work to a theoretical understanding of the problem of scalability.Most research in neural networks focuses on learning, yet it is important to understand the physical limitations of the network before the resources needed to solve a certain problem can be calculated. One of the aims of this book is to compare the complexity of neural networks and the complexity of conventional computers, looking at the computational ability and resources (neurons and time) that are a necessary part of the foundations of neural network learning.Circuit Complexity and Neural Networks contains a significant amount of background material on conventional complexity theory that will enable neural network scientists to learn about how complexity theory applies to their discipline, and allow complexity theorists to see how their discipline applies to neural networks.
(source: Nielsen Book Data)
 Publisher's summary

Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale  that is, how fast the computation time and number of neurons grow as the problem size increases. It surveys recent research in circuit complexity (a robust branch of theoretical computer science) and applies this work to a theoretical understanding of the problem of scalability.
(source: Nielsen Book Data)
Subjects
 Subjects
 Neural networks (Computer science)
 Computational complexity.
 Logic circuits.
 Réseaux neuronaux (Informatique)
 Complexité de calcul (Informatique)
 Circuits logiques.
 COMPUTERS > Enterprise Applications > Business Intelligence Tools.
 COMPUTERS > Intelligence (AI) & Semantics.
 COMPUTER SCIENCE/General
Bibliographic information
 Publication date
 1994
 Series
 Foundations of computing
 ISBN
 0585360693 (electronic bk.)
 9780585360690 (electronic bk.)
 0262281244 (electronic bk.)
 9780262281249 (electronic bk.)
 0262161486
 9780262161480