## Inductive Logic Programming: 11th International Conference, ILP 2001, Strasbourg, France, September 9-11, 2001. ProceedingsThis 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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### Contents

A Refinement Operator for Theories | 1 |

Learning Logic Programs with Neural Networks | 15 |

Classifying Uncovered Examples by Rule Stretching | 41 |

Copyright | |

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### Common terms and phrases

0-subsumption abduction accuracy active(A AdaBoost Aleph algorithm application approach arity Artificial Intelligence ARTMAP atoms background knowledge Bayesian clause Bayesian logic programs Bayesian networks bigeminy boosting CF-induction clausal theory complete Computer consequence-finding consider constraints construction cross-validation Data Mining data set database Datalog decision tree deepening operators defined denote domain encoding evaluation first-order first-order logic function given hedge heuristics hypothesis ILP systems Inductive Logic Programming inverse entailment KBNN literals LNAI Machine Learning method minimal monotonic model Muggleton negative cover neutral negative examples node OPUS parameters parse tree pattern positive examples predicate probabilistic problem Proceedings Progol Prolog propositional proteins pruning PSI-BLAST query Raedt random variables redundant refinement operator relations representation ROC curve Rouveirol rule induction Rule Stretching Section sequence solution spatial Springer-Verlag stable model structure subset SWISS-PROT Table techniques training set treelist tuple Warmr weak learner