Knowledge Representation and Organization in Machine Learning
Machine learning has become a rapidly growing field of Artificial Intelligence. Since the First International Workshop on Machine Learning in 1980, the number of scientists working in the field has been increasing steadily. This situation allows for specialization within the field. There are two typ...
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| Format: | eBook |
| Language: | English |
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Berlin, Heidelberg
Springer Berlin Heidelberg
1989, 1989
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| Edition: | 1st ed. 1989 |
| Series: | Lecture Notes in Artificial Intelligence
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| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- Explanation: A source of guidance for knowledge representation
- (Re)presentation issues in second generation expert systems
- Some aspects of learning and reorganization in an analogical representation
- A knowledge-intensive learning system for document retrieval
- Constructing expert systems as building mental models or toward a cognitive ontology for expert systems
- Sloppy modeling
- The central role of explanations in disciple
- An inference engine for representing multiple theories
- The acquisition of model-knowledge for a model-driven machine learning approach
- Using attribute dependencies for rule learning
- Learning disjunctive concepts
- The use of analogy in incremental SBL
- Knowledge base refinement using apprenticeship learning techniques
- Creating high level knowledge structures from simple elements
- Demand-driven concept formation