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Expert Systems, 2004
Abstract: Expert systems can be used to determine some objects or consequences from uncertain knowledge by hierarchical categorization. Categorical representation is psychologically motivated and also offers an explanation of how to deal with uncertain knowledge based on counting during approximate reasoning.
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Abstract: Expert systems can be used to determine some objects or consequences from uncertain knowledge by hierarchical categorization. Categorical representation is psychologically motivated and also offers an explanation of how to deal with uncertain knowledge based on counting during approximate reasoning.
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IEEE Expert, 1990
Computer risk exposures and security in general are reviewed, and factors suggesting that expert system security is a unique problem are examined. Security requirements associated with the unique characteristics of expert systems are investigated. They include technical aspects of knowledge (certainty factors, symbolic information and special fixes ...
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Computer risk exposures and security in general are reviewed, and factors suggesting that expert system security is a unique problem are examined. Security requirements associated with the unique characteristics of expert systems are investigated. They include technical aspects of knowledge (certainty factors, symbolic information and special fixes ...
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Communications of the ACM, 1988
Connectionist networks can be used as expert system knowledge bases. Furthermore, such networks can be constructed from training examples by machine learning techniques. This gives a way to automate the generation of expert systems for classification problems.
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Connectionist networks can be used as expert system knowledge bases. Furthermore, such networks can be constructed from training examples by machine learning techniques. This gives a way to automate the generation of expert systems for classification problems.
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Neural Networks, 1995
Abstract The advantages and disadvantages of classical rule-based and neural approaches to expert system design are complementary. We propose a strictly neural expert system architecture that enables the creation of the knowledge base automatically, by learning from example inferences.
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Abstract The advantages and disadvantages of classical rule-based and neural approaches to expert system design are complementary. We propose a strictly neural expert system architecture that enables the creation of the knowledge base automatically, by learning from example inferences.
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1992
We have described two expert systems which have been developed in cooperation with the mining industry. They are implemented in Prolog and are running on PCs. In these projects we have been cooperating with engineers who are interested in solutions rather than programming techniques.
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We have described two expert systems which have been developed in cooperation with the mining industry. They are implemented in Prolog and are running on PCs. In these projects we have been cooperating with engineers who are interested in solutions rather than programming techniques.
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Predicting expert system success: an expert system for expert systems
Proceedings of the 1990 ACM SIGBDP conference on Trends and directions in expert systems - SIGBDP '90, 1990Il-Yeol Song, Joseph LaGue
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An Expert System for Ironmaking
2001This paper describes an expert system for the on-line diagnosis of blast furnaces. The system analysis more than a hundred parameters in real time and shows the operator the status of the blast furnace and its behaviour. The system also gives suggestions and control set-points in order to achieve a better hot metal quality and aimed analysis, as well ...
Javier Tuya +6 more
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2004
In this paper, a multi-expert classification system (MECS), composed of two main parts performing the so-called multi-stage classification (MSC) and multi-expert classification (MEC), is proposed. The former (MSC) produces either correct decisions or the ”I do not know” (IDNK) answers, so there are not misclassifications. The latter (MEC) is a parallel
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In this paper, a multi-expert classification system (MECS), composed of two main parts performing the so-called multi-stage classification (MSC) and multi-expert classification (MEC), is proposed. The former (MSC) produces either correct decisions or the ”I do not know” (IDNK) answers, so there are not misclassifications. The latter (MEC) is a parallel
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A hybrid recommender system for an online store using a fuzzy expert system
Expert Systems With Applications, 2023Bogdan Walek
exaly