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
Available online
More options
Description
Creators/Contributors
- Author/Creator
- Parberry, Ian.
Contents/Summary
- Bibliography
- Includes bibliographical references (pages 251-257) 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
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