Machine learning: paradigms and methods
Machine learning: paradigms and methods
Learning Logical Definitions from Relations
Machine Learning
On the thresholds of knowledge
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Finding accurate frontiers: a knowledge-intensive approach to relational learning
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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There is an ongoing debate as to whether model-based orcase-based diagnostic expert systems are superior. Ourexperience has shown that the two are not mutuallyexclusive and, to the contrary, complement each other.Current expert system technology is capable of tworeasoning mechanisms, in addition to other mechanisms,integrated into one system. Depending on the knowledgeavailable, and time and cost considerations, expert systemsallow the user to decide the relative proportion of case-basedto model-based reasoning to employ in any givensituation. Diagnostic support software should be evaluatedby two critical factor groups, Ben-Bassat, et al, 1992 [1]:a) cost and time to deployment, and b) accuracy,completeness and efficiency of the diagnostic process. Inthis paper we will discuss the role of expert systems incombining model-based and case-based reasoning to effectthe most efficient user defined solution to diagnosticperformance.