Inductive Logic Programming [electronic resource] :11th International Conference, ILP 2001 Strasbourg, France, September 9–11, 2001 Proceedings /
Contributor(s): Rouveirol, Céline [editor.] | Sebag, Michéle [editor.] | SpringerLink (Online service).Material type: BookSeries: Lecture Notes in Computer Science: 2157Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2001.Description: IX, 259 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540447979.Subject(s): Computer science | Architecture, Computer | Software engineering | Computer programming | Algorithms | Mathematical logic | Artificial intelligence | Computer Science | Computer System Implementation | Software Engineering/Programming and Operating Systems | Artificial Intelligence (incl. Robotics) | Programming Techniques | Mathematical Logic and Formal Languages | Algorithm Analysis and Problem ComplexityOnline resources: Click here to access online
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.