Gradient descent training of Bayesian networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1638, Issue , 1999, Pages 190-200

  Jensen, Finn Verner ^a  

^a AALBORG UNIVERSITY   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION; GRADIENT METHODS;

ADAPTATION METHODS; GRADIENT DESCENT; GRADIENT DESCENT TRAINING; LEARNING METHODS; TRAINING METHODS; TRAINING SETS;

BAYESIAN NETWORKS;

EID: 84929241881     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48747-6_18     Document Type: Conference Paper

References (8)

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2. Finn V. Jensen. An introduction to Bayesian Networks. UCL Press Limited, 1996.
3. K. B. Laskey. Adapting connectionist learning to Bayes network. International Journal of Approximate Reasoning, (4):261-282, 1990.
4. K. B. Laskey. Sensitivity analysis for probability assessments in Bayesian networks. In David Heckerman and Abe Mamdani, editors, Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, pages 136-142. Morgan Kaufmann Publishers, 1993.
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6. Kristian G. Olesen, Steffen L. Lauritzen, and Finn V. Jensen. aHUGIN: A system creating adaptive causal probabilistic networks. In Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, pages 223-229. Morgan Kaufmann Publishers, 1992.
7. Stuart Russell, John Binder, Daphne Koller, and Keiji Kanazawa. Local learning in probabilistic networks with hidden variables. In Chris S. Mellish, editor, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, volume 2, pages 1146-1152. Morgan Kaufmann Publishers, 1995.
8. David J. Spiegelhalter and Steffen L. Lauritzen. Sequential updating of conditional probabilities on directed graphical structures. Networks, 20:579-605, 1990.