| 000 | 03015nam a2200541 i 4500 | ||
|---|---|---|---|
| 001 | 6267281 | ||
| 003 | IEEE | ||
| 005 | 20220712204618.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151223s1991 maua ob 001 eng d | ||
| 010 | _z 90028969 (print) | ||
| 020 |
_a9780262256360 _qelectronic |
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| 020 |
_z026258106X _qpaperback |
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| 020 |
_z9780262581066 _qprint |
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| 035 | _a(CaBNVSL)mat06267281 | ||
| 035 | _a(IDAMS)0b000064818b4267 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aQA76.5 _b.C61939 1991eb |
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| 082 | 0 | 0 |
_a006.3 _220 |
| 245 | 0 | 0 |
_aConnectionist symbol processing / _cedited by G.E. Hinton. |
| 250 | _a1st MIT Press ed. | ||
| 264 | 1 |
_aCambridge, Massachusetts : _bMIT Press, _c1991. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1991] |
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| 300 |
_a1 PDF (262 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 | _aSpecial issues of <i>artificial intelligence</i> | |
| 500 | _a"A Bradford book." | ||
| 500 | _a"Reprinted from Artificial intelligence, an international journal, volume 46, numbers 1-2, 1990"--T.p. verso. | ||
| 504 | _aIncludes bibliographical references and index. | ||
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aThe six contributions in Connectionist Symbol Processing address the current tension within the artificial intelligence community between advocates of powerful symbolic representations that lack efficient learning procedures and advocates of relatively simple learning procedures that lack the ability to represent complex structures effectively. The authors seek to extend the representational power of connectionist networks without abandoning the automatic learning that makes these networks interesting.Aware of the huge gap that needs to be bridged, the authors intend their contributions to be viewed as exploratory steps in the direction of greater representational power for neural networks. If successful, this research could make it possible to combine robust general purpose learning procedures and inherent representations of artificial intelligence -- a synthesis that could lead to new insights into both representation and learning. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/23/2015. | ||
| 650 | 0 |
_aConnection machines. _921912 |
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| 650 | 0 |
_aNeural networks (Computer science) _93414 |
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| 655 | 0 |
_aElectronic books. _93294 |
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| 700 | 1 |
_aHinton, Geoffrey E. _921913 |
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| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _921914 |
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| 710 | 2 |
_aMIT Press, _epublisher. _921915 |
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| 776 | 0 | 8 |
_iPrint version _z9780262581066 |
| 830 | 0 |
_aSpecial issues of <i>artificial intelligence</i> _921916 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267281 |
| 942 | _cEBK | ||
| 999 |
_c72939 _d72939 |
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