Circuit complexity and neural networks / (Record no. 39337)

000 -LEADER
fixed length control field 03162nam a2200505 i 4500
001 - CONTROL NUMBER
control field 6267424
003 - CONTROL NUMBER IDENTIFIER
control field IEEE
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20190220121647.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr |n|||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 151223s1994 maua ob 001 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780262281249
Qualifying information ebook
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 0585360693
Qualifying information electronic
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9780585360690
Qualifying information electronic
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 0262281244
Qualifying information electronic
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9780262161480
Qualifying information print
035 ## - SYSTEM CONTROL NUMBER
System control number (CaBNVSL)mat06267424
035 ## - SYSTEM CONTROL NUMBER
System control number (IDAMS)0b000064818b442c
040 ## - CATALOGING SOURCE
Original cataloging agency CaBNVSL
Language of cataloging eng
Description conventions rda
Transcribing agency CaBNVSL
Modifying agency CaBNVSL
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.87
Item number .P38 1994eb
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Parberry, Ian,
Relator term author.
245 10 - TITLE STATEMENT
Title Circuit complexity and neural networks /
Statement of responsibility, etc. Ian Parberry.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cambridge, Massachusetts :
Name of producer, publisher, distributor, manufacturer MIT Press,
Date of production, publication, distribution, manufacture, or copyright notice c1994.
264 #2 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture [Piscataqay, New Jersey] :
Name of producer, publisher, distributor, manufacturer IEEE Xplore,
Date of production, publication, distribution, manufacture, or copyright notice [1994]
300 ## - PHYSICAL DESCRIPTION
Extent 1 PDF (xxix, 270 pages) :
Other physical details illustrations.
336 ## - CONTENT TYPE
Content type term text
Source rdacontent
337 ## - MEDIA TYPE
Media type term electronic
Source isbdmedia
338 ## - CARRIER TYPE
Carrier type term online resource
Source rdacarrier
490 1# - SERIES STATEMENT
Series statement Foundations of computing
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references (p. [251]-257) and index.
506 1# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Restricted to subscribers or individual electronic text purchasers.
520 ## - SUMMARY, ETC.
Summary, etc. 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.
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Additional physical form available note Also available in print.
538 ## - SYSTEM DETAILS NOTE
System details note Mode of access: World Wide Web
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note Description based on PDF viewed 12/23/2015.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Logic circuits.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computational complexity.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Neural networks (Computer science)
655 #0 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element IEEE Xplore (Online Service),
Relator term distributor.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element MIT Press,
Relator term publisher.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version
International Standard Book Number 9780262161480
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Foundations of computing.
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified Abstract with links to resource
Uniform Resource Identifier http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267424

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