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Synopsis
In this remarkable blend of formal theory and practical application, David Heckerman develops methods for building normative expert systems—expert systems that encode knowledge in a decision-theoretic framework.
Heckerman introduces the similarity network and partition, two extensions to the influence diagram representation. He uses the new representations to construct Pathfinder, a large, normative expert system for the diagnosis of lymph-node diseases. Heckerman shows that such expert systems can be built efficiently, and that the use of a normative theory as the framework for representing knowledge can dramatically improve the quality of expertise that is delivered to the user. He concludes with a formal evaluation of the power of his methods for building normative expert systems. David Heckerman is Assistant Professor of Computer Science at the University of Southern California. He received his doctoral degree in Medical Information Sciences from Stanford University.
Contents: Introduction. Similarity Networks and Partitions: A Simple Example. Theory of Similarity Networks. Pathfinder: A Case Study. An Evaluation of Pathfinder. Conclusions and Future Work.
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- PublisherMit Pr
- Publication date1991
- ISBN 10 0262082063
- ISBN 13 9780262082068
- BindingHardcover
- LanguageEnglish
- Number of pages261
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Probabilistic Similarity Networks (ACM Doctoral Dissertation Award).
Seller: Kloof Booksellers & Scientia Verlag, Amsterdam, Netherlands
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Condition: as new. Cambridge, MA: The MIT Press, 1991. Hardcover. 261 pp.(ACM Doctoral Dissertation Award). - In this remarkable blend of formal theory and practical application, David Heckerman develops methods for building normative expert systems--expert systems that encode knowledge in a decision-theoretic framework. Heckerman introduces the similarity network and partition, two extensions to the influence diagram representation. He uses the new representations to construct Pathfinder, a large, normative expert system for the diagnosis of lymph-node diseases. Heckerman shows that such expert systems can be built efficiently, and that the use of a normative theory as the framework for representing knowledge can dramatically improve the quality of expertise that is delivered to the user. He concludes with a formal evaluation of the power of his methods for building normative expert systems. David Heckerman is Assistant Professor of Computer Science at the University of Southern California. He received his doctoral degree in Medical Information Sciences from Stanford University. English text. Condition : as new. Condition : as new copy. ISBN 9780262082068. Keywords : , artificial intelligence. Seller Inventory # 263596
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Probabilistic Similarity Networks
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The MIT Press, Cambridge, MA, 1991
ISBN 10: 0262082063
ISBN 13: 9780262082068
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Hardcover
Seller: Riverby Books, Fredericksburg, VA, U.S.A.
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Hardcover. Condition: Very Good. No Jacket. Hardcover with black paper over boards, gold decoration and white text across covers and spine. Corners bumped, top edge slightly discolored to a darker shade. No date on title page. Copyright page dated only 1991. xx + 234 pages. Includes foreword by Edward H. Shortliffe, author's preface, guide for the reader, appendices with background, proofs, glossary, and results, bibliography, index, notation, charts, and a list of MIT's other publications. A few pages (5 or fewer) show pen marks; no writing, only lines. Pages otherwise bright. Binding neat and tight. Please email us with questions or to request photos. Seller Inventory # 22-2204
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Probabilistic Similarity Networks (ACM Doctoral Dissertation Award)
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