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Download Uncertainty in Artificial Intelligence 2 (MACHINE INTELLIGENCE AND PATTERN RECOGNITION) (v. 2) fb2

by John F. Lemmer,Laveen N. Kanal

  • ISBN: 0444703969
  • Category: Technology
  • Author: John F. Lemmer,Laveen N. Kanal
  • Subcategory: Computer Science
  • Other formats: txt lit rtf mobi
  • Language: English
  • Publisher: Elsevier Science Ltd (April 1, 1988)
  • Pages: 474 pages
  • FB2 size: 1781 kb
  • EPUB size: 1151 kb
  • Rating: 4.9
  • Votes: 266
Download Uncertainty in Artificial Intelligence 2 (MACHINE INTELLIGENCE AND PATTERN RECOGNITION) (v. 2) fb2

Items related to Uncertainty in Artificial Intelligence 2 (MACHINE INTELLIGEN. This second volume is arranged in four sections: Analysis contains papers which compare the attributes of various approaches to uncertainty.

Items related to Uncertainty in Artificial Intelligence 2 (MACHINE INTELLIGEN. John F. Lemmer Uncertainty in Artificial Intelligence 2 (MACHINE INTELLIGENCE AND PATTERN RECOGNITION) (v. 2). ISBN 13: 9780444703965. Tools provides sufficient information for the reader to implement uncertainty calculations. Papers in the Theory section explain various approaches to uncertainty.

April 24, 2010 History. found in the catalog. Uncertainty in Artificial Intelligence 2 (Machine Intelligence and Pat.

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Get a full overview of Machine Intelligence and Pattern Recognition Book Series. The third in an informal series of books about parallel processing for Artificial Intelligence, this volume is based on the assumption that the computational demands of many AI tasks can be better served by parallel architectures than by the currently popular workstations. However, no assumption is made about the kind of parallelism to be used.

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Uncertainty in Artificial Intelligence 2 book.

Pattern recognition was accepted in particular areas, like machine vision, as a kind of technological fix.

July 1990 · Robotica. Toward human level machine intelligence - is it achievable? August 2008. Achievement of human level machine intelligence has long been one of the basic objectives of AI. Officially, AI was born in 1956. Pattern recognition was accepted in particular areas, like machine vision, as a kind of technological fix. But statistical and probabilistic approaches are now mainstream.

Pearl, J. Fusion, propagation, and structuring in Bayesian networks Artificial Intelligence 29, 241–288 (1986). The role of fuzzy logic in the management of uncertainty in expert systems Fuzzy Sets and Systems 11, 199–227 (1983). Recommend this journal.

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This second volume is arranged in four sections: Analysis contains papers which compare the attributes of various approaches to uncertainty. Tools provides sufficient information for the reader to implement uncertainty calculations. Papers in the Theory section explain various approaches to uncertainty. The Applications section describes the difficulties involved in, and the results produced by, incorporating uncertainty into actual systems.

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