Inductive logic programming : 11th International Conference, ILP 2001, Strasbourg, France, September 9 11, 2001 : proceedings
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| Institutionell upphovsman: | |
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| Övriga upphovsmän: | , |
| Materialtyp: | Livre numérique |
| Språk: | Anglais |
| Publicerad: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serie: | Lecture notes in computer science. Lecture notes in artificial intelligence
2157 |
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| Länkar: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Inductive logic programming, 11th International Conference, ILP 2001, Strasbourg, France, September 9-11, 2001, proceedings, Céline Rouveirol, Michèle Sebag (eds.), 2001, Berlin, Springer, 1 vol. (X-259 p.), Lecture notes in computer science, 3-540-42538-1 • Inductive Logic Programming, Texte imprimé, 9783662201572 |
Innehållsförteckning:
- A Refinement Operator for Theories
- Learning Logic Programs with Neural Networks
- A Genetic Algorithm for Propositionalization
- Classifying Uncovered Examples by Rule Stretching
- Relational Learning Using Constrained Confidence-Rated Boosting
- Induction, Abduction, and Consequence-Finding
- From Shell Logs to Shell Scripts
- An Automated ILP Server in the Field of Bioinformatics
- Adaptive Bayesian Logic Programs
- Towards Combining Inductive Logic Programming with Bayesian Networks
- Demand-Driven Construction of Structural Features in ILP
- Transformation-Based Learning Using Multirelational Aggregation
- Discovering Associations between Spatial Objects: An ILP Application
- ?-Subsumption in a Constraint Satisfaction Perspective
- Learning to Parse from a Treebank: Combining TBL and ILP
- Induction of Stable Models
- Application of Pruning Techniques for Propositional Learning to Progol
- Application of ILP to Cardiac Arrhythmia Characterization for Chronicle Recognition
- Efficient Cross-Validation in ILP
- Modelling Semi-structured Documents with Hedges for Deduction and Induction
- Learning Functions from Imperfect Positive Data.