MARC record from Internet Archive

LEADER: 05049cam 2200853Ma 4500
001 ocm47011917
003 OCoLC
005 20200922054352.0
008 010327s1994 maua ob 001 0 eng d
006 m o d
007 cr cn|||||||||
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037 $a1836$bMIT Press
037 $a9780262281249$bMIT Press
050 4 $aQA76.87$b.P38 1994eb
072 7 $aCOM$x005030$2bisacsh
072 7 $aCOM$x004000$2bisacsh
082 04 $a006.3$220
084 $aTP183$2clc
100 1 $aParberry, Ian.
245 10 $aCircuit complexity and neural networks /$cIan Parberry.
260 $aCambridge, Mass. :$bMIT Press,$c©1994.
300 $a1 online resource (xxix, 270 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aFoundations of computing
504 $aIncludes bibliographical references (pages 251-257) and index.
588 0 $aPrint version record.
520 $aNeural 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.
650 0 $aNeural networks (Computer science)
650 0 $aComputational complexity.
650 0 $aLogic circuits.
650 7 $aCOMPUTERS$xEnterprise Applications$xBusiness Intelligence Tools.$2bisacsh
650 7 $aCOMPUTERS$xIntelligence (AI) & Semantics.$2bisacsh
650 07 $aNeural networks (Computer science)$2cct
650 07 $aComputational complexity.$2cct
650 07 $aLogic circuits.$2cct
650 7 $aComputational complexity.$2fast$0(OCoLC)fst00871991
650 7 $aLogic circuits.$2fast$0(OCoLC)fst01002031
650 7 $aNeural networks (Computer science)$2fast$0(OCoLC)fst01036260
650 7 $aEngineering & Applied Sciences.$2hilcc
650 7 $aComputer Science.$2hilcc
653 $aCOMPUTER SCIENCE/General
655 0 $aElectronic books.
655 4 $aElectronic books.
776 08 $iPrint version:$aParberry, Ian.$tCircuit complexity and neural networks.$dCambridge, Mass. : MIT Press, ©1994$z0262161486$w(DLC) 94007955$w(OCoLC)30078959
830 0 $aFoundations of computing.
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856 40 $3MIT Press$uhttp://mitpress.mit.edu/9780262161480
856 40 $3MIT Press$uhttps://doi.org/10.7551/mitpress/1836.001.0001
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