Algorithmic learning for knowledge-based systems : GOSLER final report
This book is the final report on a comprehensive basic research project, named GOSLER on algorithmic learning for knowledge-based systems supported by the German Federal Ministry of Research and Technology during the years 1991 - 1994. This research effort was focused on the study of fundamental lea...
Gespeichert in:
| 1. Verfasser: | |
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| Weitere Verfasser: | |
| Format: | Livre numérique |
| Sprache: | Anglais |
| Veröffentlicht: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Schriftenreihe: | Lecture notes in computer science. Lecture notes in artificial intelligence
961 |
| Schlagworte: | |
| Online Zugang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Anmerkung: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Algorithmic learning for knowledge-based systems, GOSLER final report, Klaus P. Jantke, Steffen Lange, eds, Berlin, Springer-Verlag, 1995, 1 vol. (X-510 p.), Lecture notes in computer science, 3-540-60217-8 • Algorithmic Learning for Knowledge-Based Systems, Texte imprimé, 9783662168653 |
Inhaltsangabe:
- Learning and consistency
- Error detecting in inductive inference
- Learning from good examples
- Towards reduction arguments for FINite learning
- Not-so-nearly-minimal-size program inference (preliminary report)
- Optimization problem in inductive inference
- On identification by teams and probabilistic machines
- Topological considerations in composing teams of learning machines
- Probabilistic versus deterministic memory limited learning
- Classification using information
- Classifying recursive predicates and languages
- A guided tour across the boundaries of learning recursive languages
- Pattern inference
- Inductive learning of recurrence-term languages from positive data
- Learning formal languages based on control sets
- Learning in case-based classification algorithms
- Optimal strategies Learning from examples Boolean equations
- Feature construction during tree learning
- On lower bounds for the depth of threshold circuits with weights from {?1,0,+1}
- Structuring neural networks and PAC-Learning
- Inductive synthesis of rewrite programs
- TLPS A term rewriting laboratory (not only) for experiments in automatic program synthesis
- GoslerP A logic programming tool for inductive inference.