Inductive Logic Programming de Stephen Muggleton

Inductive Logic Programming

Stephen Muggleton

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 included is the invited contribution "Inductive logic programming for natural language processing" by Raymond J. Mooney. Among the topics covered are natural language learning, drug design, NMR and ECG analysis, glaucoma diagnosis, efficiency measures for implementations and database interaction, program synthesis, proof encoding and learning in the absence of negative data, and least generalizations under implication ordering.

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Cuprins

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.