Inductive logic programming : 6th International Workshop, ILP-96, Stockholm, Sweden, August 26-28, 1996 : selected papers
This book constitutes the strictly refereed post-workshop proceedings of the 6th International Workshop on Inductive Logic Programming, ILP-96, held in Stockholm, Sweden, in August 1996. The 21 full papers were carefully reviewed and selected for inclusion in the book in revised version. Also includ...
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| Format: | Livre numérique |
| Sprache: | Anglais |
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| Schriftenreihe: | Lecture notes in computer science. Lecture notes in artificial intelligence
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| Edition sous un autre format: | • Inductive logic programming, 6th International Workshop, ILP-96, Stockholm, Sweden, August 1996, selected papers, Stephen Muggleton, (ed.), 1997, New York, Springer, 1 vol. (VIII-396 p.), Lecture notes in computer science, 3-540-63494-0 • Inductive Logic Programming, Texte imprimé, 9783662186947 |
Inhaltsangabe:
- Inductive logic programming for natural language processing
- An initial experiment into stereochemistry-based drug design using inductive logic programming
- Applying ILP to diterpene structure elucidation from 13C NMR spectra
- Analysis and prediction of piano performances using inductive logic programming
- Noise detection and elimination applied to noise handling in a KRK chess endgame
- Feature construction with inductive logic programming: A study of quantitative predictions of biological activity by structural attributes
- Polynomial-time learning in logic programming and constraint logic programming
- Analyzing and learning ECG waveforms
- Learning rules that classify ocular fundus images for glaucoma diagnosis
- A new design and implementation of progol by bottom-up computation
- Inductive logic program synthesis with DIALOGS
- Relational knowledge discovery in databases
- Efficient ?-subsumption based on graph algorithms
- Integrity constraints in ILP using a Monte Carlo approach
- Restructuring chain datalog programs
- Top-down induction of logic programs from incomplete samples
- Least generalizations under implication
- Efficient proof encoding
- Learning Logic programs with random classification noise
- Handling Quantifiers in ILP
- Learning from positive data
- ?-Subsumption and its application to learning from positive-only examples.