Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Rule-based Classification Procedures Related to the Unprecisely Formulated Expert Rules
SIBGRAPHI '98 Proceedings of the International Symposium on Computer Graphics, Image Processing, and Vision
Consistency conditions of the expert rule set in the probabilistic pattern recognition
CIS'04 Proceedings of the First international conference on Computational and Information Science
Fusion of rule-based and sample-based classifiers: probabilistic approach
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Combining rule-based and sample-based classifiers – probabilistic approach
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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The present paper is devoted to the pattern recognition procedures that simultaneously use the information contained in the empirical data (learning set) and the set of expert rules with unprecisely formulated weights understood as conditional probabilities. Adopting the probabilistic model the combined and unified recognition algorithms are derived. In the first approach algorithm is based simply on the both set of data, in the second however, one set of data is transformed into the second one. Proposed algorithms were applied practically to the diagnosis of acute renal failure in children. Obtained results have proved its effectiveness in the computer medical decisionmaking.