| 000 | 03166nam a2200529 i 4500 | ||
|---|---|---|---|
| 001 | 6276852 | ||
| 003 | IEEE | ||
| 005 | 20220712204750.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151229s2001 maua ob 001 eng d | ||
| 010 | _z 2001030212 (print) | ||
| 020 |
_a9780262291200 _qelectronic |
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| 020 |
_z9780262600422 _qprint |
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| 020 |
_z0262600420 _qpbk. : alk. paper |
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| 035 | _a(CaBNVSL)mat06276852 | ||
| 035 | _a(IDAMS)0b000064818c1f8f | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aQA76.87 _b.G72 2001eb |
|
| 082 | 0 | 0 |
_a006.3/2 _221 |
| 245 | 0 | 0 |
_aGraphical models : _bfoundations of neural computation / _cedited by Michael I. Jordan and Terrence J. Sejnowski. |
| 264 | 1 |
_aCambridge, Massachusetts : _bMIT Press, _cc2001. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2001] |
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| 300 |
_a1 PDF (xxiv, 421 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 | _aComputational neuroscience | |
| 500 | _a"A Bradford book." | ||
| 500 | _aIncludes bibliographical references and index. | ||
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aGraphical models use graphs to represent and manipulate joint probability distributions. They have their roots in artificial intelligence, statistics, and neural networks. The clean mathematical formalism of the graphical models framework makes it possible to understand a wide variety of network-based approaches to computation, and in particular to understand many neural network algorithms and architectures as instances of a broader probabilistic methodology. It also makes it possible to identify novel features of neural network algorithms and architectures and to extend them to more general graphical models.This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research.Contributors H. Attias, C. M. Bishop, B. J. Frey, Z. Ghahramani, D. Heckerman, G. E. Hinton, R. Hofmann, R. A. Jacobs, Michael I. Jordan, H. J. Kappen, A. Krogh, R. Neal, S. K. Riis, F. B. Rodr�iguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/29/2015. | ||
| 650 | 0 |
_aNeural networks (Computer science) _93414 |
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| 650 | 0 |
_aComputer graphics. _94088 |
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| 655 | 0 |
_aElectronic books. _93294 |
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| 700 | 1 |
_aJordan, Michael Irwin, _d1956- _923647 |
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| 700 | 1 |
_aSejnowski, Terrence J. _q(Terrence Joseph) _923648 |
|
| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _923649 |
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| 710 | 2 |
_aMIT Press, _epublisher. _923650 |
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| 776 | 0 | 8 |
_iPrint version: _z9780262600422 |
| 830 | 0 |
_aComputational neuroscience _922622 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6276852 |
| 942 | _cEBK | ||
| 999 |
_c73248 _d73248 |
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