| 刊年 | 2001 |
| G/SMD | リモートファイル |
| 形態 | 1 online resource (xxiv, 421 p.) : ill. |
| シリーズ名 | Computational neuroscience
|
| 注記 | "A Bradford book."
Includes bibliographical references and index
Restricted to subscribers or individual electronic text purchasers
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. Rodrƒiguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss
Also available in print
Mode of access: World Wide Web
Description based on PDF viewed 12/29/2015
URL:https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6276852(Abstract with links to resource) |
| 出版国 | アメリカ合衆国 |
| 標題言語 | 英語 |
| 本文言語 | 英語 |
| 著者情報 | Jordan, Michael Irwin
|
| ISBN | 9780262291200(: electronic bk)
|
| 無効/取消ISBN | 9780262600422(: electronic bk)
|
| 件名 | LCSH:Neuralnetworks(Computerscience)
LCSH:Computergraphics
|
| NCID | 6276852 |
| IDENT | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6276852 |