Machine learning of inductive bias
Machine learning of inductive bias
Applied Artificial Intelligence
Diagnosis with a function-fault model
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Integrating classification-based compiled level reasoning with function-based deep level reasoning
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Mesicar-a medical expert system integrating causal and associative reasoning
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KARDIO: a study in deep and qualitative knowledge for expert systems
KARDIO: a study in deep and qualitative knowledge for expert systems
AI Magazine
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Explanation-Based Generalization: A Unifying View
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EWSL '91 Proceedings of the European Working Session on Machine Learning
Mdx2: an integrated medical diagnostic system
Mdx2: an integrated medical diagnostic system
The use of explanations for similarity-based learning
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
LEAP: a learning apprentice for VLSI design
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Generate, test and debug: combining associational rules and causal models
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Artificial Intelligence in Medicine
Guest editorial: Knowledge-based data analysis and interpretation
Artificial Intelligence in Medicine
Paper: Learning and discovery from a clinical database: An incremental concept formation approach
Artificial Intelligence in Medicine
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MESICAR is a second generation expert system which contains very general descriptions of rheumatological disorders in the primary medical care field. With the help of a detailed hierarchical description of the human anatomy the system is able to support diagnostic decisions. The paper describes how machine learning techniques are used to automatically construct more specific disease descriptions for common, frequently occurring cases. The system MESICAR-LEARN implements a learning method which integrates analytical and empirical learning techniques. Cases diagnosed by MESICAR form the training examples, and MESICAR's knowledge base is used as domain theory. The leamed concepts are integrated into a hierarchy of disease descriptions. They support efficient and fast reasoning on common cases in addition to the general diagnostic support afforded by MESICAR's deep knowledge.