Inductive Logic Programming (eBook, PDF)

11th International Conference, ILP 2001, Strasbourg, France, September 9-11, 2001. Proceedings
Redaktion: Rouveirol, Celine; Sebag, Michele

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 11th International Conference on Inductive Logic Programming, ILP 2001, held in Strasbourg, France in September 2001.The 21 revised full papers presented were carefully reviewed and selected from 37 submissions. Among the topics addressed are data mining issues for multi-relational databases, supervised learning, inductive inference, Bayesian reasoning, learning refinement operators, neural network learning, constraint satisfaction, genetic algorithms, statistical machine learning, transductive inference, etc.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 259
• Erscheinungstermin: 30. Juni 2003
• Englisch
• ISBN-13: 9783540447979
• Artikelnr.: 53385805

Inhaltsangabe

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.