| 000 | 03235nam a2200517 i 4500 | ||
|---|---|---|---|
| 001 | 6267424 | ||
| 003 | IEEE | ||
| 005 | 20220712204701.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151223s1994 maua ob 001 eng d | ||
| 020 |
_a9780262281249 _qebook |
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| 020 |
_z0585360693 _qelectronic |
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| 020 |
_z9780585360690 _qelectronic |
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| 020 |
_z0262281244 _qelectronic |
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| 020 |
_z9780262161480 _qprint |
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| 035 | _a(CaBNVSL)mat06267424 | ||
| 035 | _a(IDAMS)0b000064818b442c | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aQA76.87 _b.P38 1994eb |
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| 100 | 1 |
_aParberry, Ian, _eauthor. _922740 |
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| 245 | 1 | 0 |
_aCircuit complexity and neural networks / _cIan Parberry. |
| 264 | 1 |
_aCambridge, Massachusetts : _bMIT Press, _cc1994. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1994] |
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| 300 |
_a1 PDF (xxix, 270 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 | _aFoundations of computing | |
| 504 | _aIncludes bibliographical references (p. [251]-257) and index. | ||
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 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. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/23/2015. | ||
| 650 | 0 |
_aLogic circuits. _93501 |
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| 650 | 0 |
_aComputational complexity. _93729 |
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| 650 | 0 |
_aNeural networks (Computer science) _93414 |
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| 655 | 0 |
_aElectronic books. _93294 |
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| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _922741 |
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| 710 | 2 |
_aMIT Press, _epublisher. _922742 |
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| 776 | 0 | 8 |
_iPrint version _z9780262161480 |
| 830 | 0 |
_aFoundations of computing. _922604 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267424 |
| 942 | _cEBK | ||
| 999 |
_c73078 _d73078 |
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