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...
Zapisane w:
| 1. autor: | |
|---|---|
| Format: | Livre numérique |
| Język: | Anglais |
| Wydane: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Seria: | Lecture notes in computer science. Lecture notes in artificial intelligence
347 |
| Hasła przedmiotowe: | |
| Dostęp online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Komentarz: |
Actes d'un séminaire tenu à Eringerfeld en 1987, d'après la préface Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Knowledge representation and organization in machine learning, K. Morik (ed.), Berlin, Springer-Verlag, 1989, 1 vol. (XIII-319 p.), Lecture notes in computer science, 3-540-50768-X • Knowledge Representation and Organization in Machine Learning, Texte imprimé, 9783662201268 |
Spis treści:
- 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.