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Record 3 of 3
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You searched IISERK - Title: Prentice Hall molecular model set for general and organic chemistry [model].
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Call Number
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006.3
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Title
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Parallel processing for artificial intelligence 3 [electronic resource] / edited by James Geller, Hiroaki Kitano, Christian B. Suttner.
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Publication
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Amsterdam ; New York : Elsevier, 1997.
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Material Info.
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x, 345 p. : ill. ; 23 cm.
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Series
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Machine intelligence and pattern recognition ; v. 20
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Summary Note
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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. Transputers, Connection Machines, farms of workstations, Cellular Neural Networks, Crays, and other hardware paradigms of parallelism are used by the authors of this collection.The papers arise from the areas of parallel knowledge representation, neural modeling, parallel non-monotonic reasoning, search and partitioning, constraint satisfaction, theorem proving, parallel decision trees, parallel programming languages and low-level computer vision. The final paper is an experience report about applications of massive parallelism which can be said to capture the spirit of a whole period of computing history. This volume provides the reader with a snapshot of the state of the art in Parallel Processing for Artificial Intelligence.
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Notes
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Includes bibliographical references.
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Notes
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Electronic reproduction. Amsterdam : Elsevier Science & Technology, 2007.
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ISBN
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9780444824868
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ISBN
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0444824863
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Subject
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Parallel processing (Electronic computers)
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Subject
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Artificial intelligence.
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Subject
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Electronic books.
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Added Entry
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Geller, James.
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Added Entry
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Kitano, Hiroaki, 1961-
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Added Entry
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Suttner, Christian B.
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Added Entry
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ScienceDirect (Online service)
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Date
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Year, Month, Day:01405141
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Link
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An electronic book accessible through the World Wide Web; click for information ScienceDirect
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