Graphical models : foundations of neural computation

Graphical 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-bas...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Jordan, Michael Irwin - 1956- Sejnowski, Terrence Joseph

Format:	E-Book
Sprache:	Englisch

Erschienen:	Cambridge, Mass: MIT Press ; c2001

Schlagwörter:	Computer graphics Neural networks (Computer science)

Anmerkung:	"A Bradford book Includes bibliographical references and index
Umfang:	Online-Ressource (xxiv, 421 p) ; ill

Reproduktion:	Online-Ausg.

Reihe:	Computational neuroscience

Links:	Link aufrufen IEEE Xplore Zentralblatt MATH Inhaltsverzeichnis
ISBN:	0-262-29120-7 0-262-60042-0 978-0-262-29120-0 978-0-262-60042-2

Katalog-ID:	81666708X

Internformat


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Indexfelder

matchkey_str	book:9780262291200:2001----
oclc_num	827013364
hierarchy_sort_str	c2001
callnumber-subject-code	QA
publishDate	2001
allfields	0262291207 electronic bk. 0-262-29120-7 0262600420 0-262-60042-0 9780262291200 : electronic bk. 978-0-262-29120-0 9780262600422 978-0-262-60042-2 (DE-627)81666708X (DE-576)9816667088 (DE-599)GBV81666708X (OCoLC)827013364 (ZBM)1058.68097 (ZBM)1058.68097 (MITPRESS)6276852 (EBP)055121144 DE-627 ger DE-627 rakwb eng XD-US QA76.87 *68T05 msc 68-01 msc 68R10 msc 68W05 msc Graphical models foundations of neural computation edited by Michael I. Jordan and Terrence J. Sejnowski Cambridge, Mass MIT Press c2001 Online-Ressource (xxiv, 421 p) ill Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Computational neuroscience "A Bradford book Includes bibliographical references and index 1Probabilistic Independence Networks for Hidden Markov Probability Models Padhraic Smyth, David Heckerman, Michael I. Jordan12Learning and Relearning in Boltzmann Machines Graphical 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. Rodriguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss. Online-Ausg. Computer graphics Neural networks (Computer science) Neural networks (Computer science) Computer graphics Jordan, Michael Irwin 1956- oth Sejnowski, Terrence Joseph oth 9780262600422 Print version Graphical models (DLC)2001030212 https://ieeexplore.ieee.org/book/6276852 X:MITPRESS Verlag IEEE Xplore lizenzpflichtig Volltext https://zbmath.org/?q=an:1058.68097 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext http://www.gbv.de/dms/bowker/toc/9780262600422.pdf V:DE-601 X:Bowker pdf/application 2015-03-18 Verlag Inhaltsverzeichnis Inhaltsverzeichnis ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 045F 006.3/2 22 01 0018 3848470020 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 1521012482 olr-MIT i z 31-01-15 100 01 3100 4472463563 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011216534 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 374074975X 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6276852 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6276852 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6276852 100 01 3100 https://ieeexplore.ieee.org/book/6276852 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6276852 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6276852 2015 01 DE-93 https://ieeexplore.ieee.org/book/6276852 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 23 01 0830 2015.01.31 370 01 4370 2021.12.01
spelling	0262291207 electronic bk. 0-262-29120-7 0262600420 0-262-60042-0 9780262291200 : electronic bk. 978-0-262-29120-0 9780262600422 978-0-262-60042-2 (DE-627)81666708X (DE-576)9816667088 (DE-599)GBV81666708X (OCoLC)827013364 (ZBM)1058.68097 (ZBM)1058.68097 (MITPRESS)6276852 (EBP)055121144 DE-627 ger DE-627 rakwb eng XD-US QA76.87 *68T05 msc 68-01 msc 68R10 msc 68W05 msc Graphical models foundations of neural computation edited by Michael I. Jordan and Terrence J. Sejnowski Cambridge, Mass MIT Press c2001 Online-Ressource (xxiv, 421 p) ill Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Computational neuroscience "A Bradford book Includes bibliographical references and index 1Probabilistic Independence Networks for Hidden Markov Probability Models Padhraic Smyth, David Heckerman, Michael I. Jordan12Learning and Relearning in Boltzmann Machines Graphical 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. Rodriguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss. Online-Ausg. Computer graphics Neural networks (Computer science) Neural networks (Computer science) Computer graphics Jordan, Michael Irwin 1956- oth Sejnowski, Terrence Joseph oth 9780262600422 Print version Graphical models (DLC)2001030212 https://ieeexplore.ieee.org/book/6276852 X:MITPRESS Verlag IEEE Xplore lizenzpflichtig Volltext https://zbmath.org/?q=an:1058.68097 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext http://www.gbv.de/dms/bowker/toc/9780262600422.pdf V:DE-601 X:Bowker pdf/application 2015-03-18 Verlag Inhaltsverzeichnis Inhaltsverzeichnis ZDB-37-IEM 2012 GBV_ILN_22 ISIL_DE-18 SYSFLAG_1 GBV_KXP SSG-OPC-MAT GBV_ILN_22_i22818 GBV_ILN_23 ISIL_DE-830 GBV_ILN_100 ISIL_DE-Ma9 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2015 ISIL_DE-93 BO 045F 006.3/2 22 01 0018 3848470020 olrm-h228-MITIEEE zi22818 03-02-21 23 01 0830 1521012482 olr-MIT i z 31-01-15 100 01 3100 4472463563 09 --%%-- eBook MIT Press --%%-- --%%-- OLR-MIT-CEC Vervielfältigungen (z.B. Kopien, Downloads) sind nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. z 30-01-24 370 01 4370 4011216534 olr-ebook mitieee Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 01-12-21 2015 01 DE-93 374074975X 00 --%%-- --%%-- p --%%-- Campuslizenz l01 18-08-20 22 01 0018 Volltextzugang Campus https://ieeexplore.ieee.org/book/6276852 22 01 0018 Nur für Angehörige der Universität Hamburg: Volltextzugang von außerhalb des Campus http://emedien.sub.uni-hamburg.de/han/ieee/ieeexplore.ieee.org/book/6276852 23 01 0830 MIT Press EBook https://ieeexplore.ieee.org/book/6276852 100 01 3100 https://ieeexplore.ieee.org/book/6276852 100 01 3100 für Uniangehörige: Zugang weltweit http://han.med.uni-magdeburg.de/han/mitvia-ieee/ieeexplore.ieee.org/book/6276852 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://ieeexplore.ieee.org/book/6276852 2015 01 DE-93 https://ieeexplore.ieee.org/book/6276852 23 01 0830 2018-01805, 2018-01806, 2018-01808 22 01 0018 olrm-h228-MITIEEE 23 01 0830 olr-MIT 100 01 3100 OLR-MIT-CEC 370 01 4370 olr-ebook mitieee 23 01 0830 2015.01.31 370 01 4370 2021.12.01
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abstract	Graphical 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. Rodriguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss. "A Bradford book Includes bibliographical references and index
abstractGer	Graphical 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. Rodriguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss. "A Bradford book Includes bibliographical references and index
abstract_unstemmed	Graphical 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. Rodriguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss. "A Bradford book Includes bibliographical references and index
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title_short	Graphical models
url	https://ieeexplore.ieee.org/book/6276852 https://zbmath.org/?q=an:1058.68097 http://www.gbv.de/dms/bowker/toc/9780262600422.pdf
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author2	Jordan, Michael Irwin 1956- Sejnowski, Terrence Joseph
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up_date	2024-07-25T08:56:25.434Z
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fulltext	Series Foreword p. vii Sources p. ix Introduction p. xi Probabilistic Independence Networks for Hidden Markov Probability Models … Learning and Relearning in Boltzmann Machines … Learning in Boltzmann Trees … Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space … Attractor Dynamics in Feedforward Neural Networks … Efficient Learning in Boltzmann Machines Using Linear Response Theory … Asymmetric Parallel Boltzmann Machines Are Belief Networks … Variational Learning in Nonlinear Gaussian Belief Networks … Mixtures of Probabilistic Principal Component Analyzers … Independent Factor Analysis … Hierarchical Mixtures of Experts and the EM Algorithm … Hidden Neural Networks … Variational Learning for Switching State-Space Models … Nonlinear Time-Series Prediction with Missing and Noisy Data … Correctness of Local Probability Propagation in Graphical Models with Loops … Index …
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