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Литература

361. Dean T. and Kanazawa K. (1989b) A model for reasoning about persistence and causation.Computational Intelligence, 5(3), p. 142-150.

362. Dean Т., Kanazawa K., and Shewchuk J. (1990) Prediction, observation and estimation in planningand control. In 5th IEEE International Symposium on Intelligent Control, Vol. 2, p. 645—650, Los Alamitos, С A. IEEE Computer Society Press.

363. DeanT. and Wellman M. P. (1991) Planning and Control. Morgan Kaufmann, San Mateo, California.

364. Debevec P., Taylor C, and Malik J. (1996) Modeling and rendering architecture from photographs:a hybrid geometry- and image-based approach. In Proceedings of the 23rd Annual Conference on Computer Graphics (SIGGRAPH), p. 11-20.

365. Debreu G. (1960) Topological methods in cardinal utility theory. In Arrow K. J., Karlin S., andSuppes P. (Eds.), Mathematical Methods in the Social Sciences, 1959. Stanford University Press, Stanford, California.

366. Dechter R. (1990a) Enhancement schemes for constraint processing: Backjumping, learning andcutset decomposition. Artificial Intelligence, 41, p. 273-312.

367. Dechter R. (1990b) On the expressiveness of networks with hidden variables. In Proceedings of theEighth National Conference on Artificial Intelligence (AAAI-90), p. 379-385, Boston. MIT Press.

368. Dechter R. (1992) Constraint networks. In Shapiro S. (Ed.), Encyclopedia of Artificial Intelligence (2ndedition), p. 276-285. Wiley and Sons, New York.

369. Dechter R. (1999) Bucket elimination: A unifying framework for reasoning. Artificial Intelligence, 113,p. 41-85.

370. Dechter R. and Frost D. (1999) Backtracking algorothms for constraint satisfaction problems. Tech.rep., Department of Information and Computer Science, University of California, Irvine.

371. Dechter R. and Pearl J. (1985) Generalized best-first search strategies and the optimality of A*.Journal of the Association for Computing Machinery, 32(3), p. 505-536.

372. Dechter R. and Pearl J. (1987) Network-based heuristics for constraint-satisfaction problems. ArtificialIntelligence, 34(1), p. 1-38.

373. Dechter R. and Pearl J. (1989) Tree clustering for constraint networks. Artificial Intelligence, 38(3),p. 353-366.

374. DeCoste D. and Scholkopf B. (2002) Training invariant support vector machines. Machine Learning,46(1),p. 161-190.

375. Dedekind R. (1888) Was sind und was sollen dieZahlen. Braunschweig, Germany.

376. Deerwester S. C, Dumais S. Т., Landauer T. K., Furnas G. W., and Harshman R. A. (1990) Indexing bylatent semantic analysis. Journal of the American Society of Information Science, 41(6), p. 391-407.

377. DeGroot M. H. (1970) Optimal Statistical Decisions. McGraw-Hill, New York.

378. DeGroot M. H. (1989) Probability and Statistics (2nd edition). Addison-Wesley, Reading,Massachusetts.

379. DeJong G. (1981) Generalizations based on explanations. In Proceedings of the Seventh InternationalJoint Conference on Artificial Intelligence (IJCAI-81), p. 67-69, Vancouver, British Columbia. Morgan Kaufmann.

380. DeJong G. (1982) An overview of the FRUMP system. In Lehnert W. and Ringle M. (Eds.), Strategiesfor Natural Language Processing, p. 149-176. Lawrence Erlbaum, Potomac, Maryland.