Inductive Logic Programming [electronic resource] : 11th International Conference, ILP 2001 Strasbourg, France, September 9–11, 2001 Proceedings /

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.

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Bibliographic Details
Main Authors: Rouveirol, Céline. editor., Sebag, Michéle. editor., SpringerLink (Online service)
Format: Texto biblioteca
Language:eng
Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2001
Subjects: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 Complexity.,
Online Access:http://dx.doi.org/10.1007/3-540-44797-0
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Summary: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.