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5,423
European expert network for the reduction of uncertainties in
"... European expert network for the reduction of uncertainties in severe accident safety issues (EURSAFE) ..."
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European expert network for the reduction of uncertainties in severe accident safety issues (EURSAFE)
A Bayesian method for the induction of probabilistic networks from data
- MACHINE LEARNING
, 1992
"... This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabili ..."
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Cited by 1400 (31 self)
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of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief
Fusion, Propagation, and Structuring in Belief Networks
- ARTIFICIAL INTELLIGENCE
, 1986
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
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Cited by 484 (8 self)
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Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used
Ontology Development 101: A Guide to Creating Your First Ontology
, 2001
"... In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of Artificial-Intelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web ..."
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Cited by 830 (5 self)
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In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of Artificial-Intelligence laboratories to the desktops of domain experts. Ontologies have become common on the World
Probabilistic Inference Using Markov Chain Monte Carlo Methods
, 1993
"... Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces. R ..."
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Cited by 736 (24 self)
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of the range of methods that have been developed, including techniques from the varied literature that have not yet seen wide application in artificial intelligence, but which appear relevant. As illustrative examples, I use the problems of probabilistic inference in expert systems, discovery of latent classes
Rule Refinement using Expert Networks
"... Expert networks are networks of neural objects derived from expert systems. The hybrid nature of such networks allows the expert knowledge to be refined and augmented using sample data. The benefit of combining expert systems with neural network-like learning from data has been illustrated in a numb ..."
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Expert networks are networks of neural objects derived from expert systems. The hybrid nature of such networks allows the expert knowledge to be refined and augmented using sample data. The benefit of combining expert systems with neural network-like learning from data has been illustrated in a
Expert Networks: Paradigmatic Conflict, Technological Rapprochement
- Minds and Machines
, 1993
"... . A rule-based expert system is demonstrated to have both a symbolic computational network representation and a sub-symbolic connectionist representation. These alternate views enhance the usefulness of the original system by facilitating introduction of connectionist learning methods into the symbo ..."
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Cited by 10 (1 self)
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. A rule-based expert system is demonstrated to have both a symbolic computational network representation and a sub-symbolic connectionist representation. These alternate views enhance the usefulness of the original system by facilitating introduction of connectionist learning methods
Rontogiannis " An Expert Network Analyzer
- Proceedings of the 35th Canadian Cable 10. Sugawara, T., "A Cooperative LAN Diagnostic Television Association Annual Convention and Observation Expert System," in Proceedings
, 1989
"... ABSTRACTt Section 5 concludes this work and discusses future developments. In this work, we shall present the framework of an expert system that will be capable of diagnosing and predicting faults in a cable television plant. 2. STRUCTURE OF THE NETWORK The structure of the diagnostic expert system ..."
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Cited by 2 (2 self)
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ABSTRACTt Section 5 concludes this work and discusses future developments. In this work, we shall present the framework of an expert system that will be capable of diagnosing and predicting faults in a cable television plant. 2. STRUCTURE OF THE NETWORK The structure of the diagnostic expert system
An expert network simulation and design system
"... We describe the essential features of an expert system that helps nonexpert designers analyze and design computer networks. The major components of this expert system are the executive, the knowledge management system, the user interface system, the modeler, the analyzer and the synthesizer. A combi ..."
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We describe the essential features of an expert system that helps nonexpert designers analyze and design computer networks. The major components of this expert system are the executive, the knowledge management system, the user interface system, the modeler, the analyzer and the synthesizer. A
A Tutorial on Learning Bayesian Networks
- Communications of the ACM
, 1995
"... We examine a graphical representation of uncertain knowledge called a Bayesian network. The representation is easy to construct and interpret, yet has formal probabilistic semantics making it suitable for statistical manipulation. We show how we can use the representation to learn new knowledge by c ..."
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Cited by 365 (12 self)
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by combining domain knowledge with statistical data. 1 Introduction Many techniques for learning rely heavily on data. In contrast, the knowledge encoded in expert systems usually comes solely from an expert. In this paper, we examine a knowledge representation, called a Bayesian network, that lets us have
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