Neural network learning and expert systems
Neural network learning and expert systems
Building explanations from rules and structured cases
International Journal of Man-Machine Studies - AI and legal reasoning. Part 1
CABARET: rule interpretation in a hybrid architecture
International Journal of Man-Machine Studies - AI and legal reasoning. Part 1
Learning by analogical reasoning in general problem-solving
Learning by analogical reasoning in general problem-solving
Case-based reasoning
Unifying instance-based and rule-based induction
Machine Learning
Improving accuracy by combining rule-based and case-based reasoning
Artificial Intelligence
Artificial nonmonotonic neural networks
Artificial Intelligence
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
A Recency Inference Engine for Connectionist Knowledge Bases
Applied Intelligence
Rule-Induction and Case-Based Reasoning: Hybrid Architectures Appear Advantageous
IEEE Transactions on Knowledge and Data Engineering
An Efficient Hybrid Rule Based Inference Engine with Explanation Capability
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Modular Neuro-Fuzzy Networks Used in Explicit and Implicit Knowledge Integration
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Integrating case-based and rule-based reasoning in knowledge-based systems development
Integrating case-based and rule-based reasoning in knowledge-based systems development
A Fuzzy Hybrid Intelligent System for Human Semen Analysis
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Case-Based Reasoning to explain medical model exceptions
International Journal of Advanced Intelligence Paradigms
Expert Systems with Applications: An International Journal
ISOR-2: a case-based reasoning system to explain exceptional dialysis patients
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
NEST: a compositional approach to rule-based and case-based reasoning
Advances in Artificial Intelligence
Extending a hybrid CBR-ANN model by modeling predictive attributes using fuzzy sets
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
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In this paper, we present an approach integrating neurule-based and case-based reasoning. Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist representation (adaline unit). Each neurule is represented as an adaline unit. One way that the neurules can be produced is from symbolic rules by merging the symbolic rules having the same conclusion. In this way, the number of rules in the rule base is decreased. If the symbolic rules, acting as source knowledge of the neurules, do not cover the full complexities of the domain, accuracy of the produced neurules is affected as well. To improve accuracy, neurules can be integrated with cases representing their exceptions. The integration approach enhances a previous method integrating symbolic rules with cases. The use of neurules instead of symbolic rules improves the efficiency of the inference mechanism and allows for drawing conclusions even if some of the inputs are unknown.