ENGLISH

Probabilistic Similarity Networks

By David Heckerman

Book information

Publisher
The MIT Press
Year
1991
ISBN
0262082063, 9780262082068
LCC
R859.7.A78 H43 1991
Open Library ID
OL1550064M
Language
english
Format
DJVU
Filesize
2 MB (2149690 bytes)
Series
ACM Doctoral Dissertation Award
Pages
252\252
DPI
300
Time added
2011-01-23 12:00:00

Description

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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