Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Generalized subsumption and its applications to induction and redundancy
Artificial Intelligence
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Essentials of logic programming
Essentials of logic programming
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Learning nonrecursive definitions of relations with LINUS
EWSL-91 Proceedings of the European working session on learning on Machine learning
PAC-learnability of determinate logic programs
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Machine Learning - special issue on inductive logic programming
Machine Learning - special issue on inductive logic programming
Top-down induction of first-order logical decision trees
Artificial Intelligence
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Genome scale prediction of protein functional class from sequence using data mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
Algorithmic Program DeBugging
Inductive Logic Programming: From Machine Learning to Software Engineering
Inductive Logic Programming: From Machine Learning to Software Engineering
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Ilp: a short look back and a longer look forward
The Journal of Machine Learning Research
Statistical Relational Learning for Document Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Named entity learning and verification: expectation maximization in large corpora
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Relational peculiarity-oriented mining
Data Mining and Knowledge Discovery
Induction of Fuzzy and Annotated Logic Programs
Inductive Logic Programming
Learning Different User Profile Annotated Rules for Fuzzy Preference Top-k Querying
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
An l 1 Regularization Framework for Optimal Rule Combination
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Combining clauses with various precisions and recalls to produce accurate probabilistic estimates
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Ordinal classification with monotonicity constraints
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Feature selection for dimensionality reduction
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
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Inductive logic programming (ILP) is concerned with the development of techniques and tools for relational data mining. Besides the ability to deal with data stored in multiple tables, ILP systems are usually able to take into account generally valid background (domain) knowledge in the form of a logic program. They also use the powerful language of logic programs for describing discovered patterns. This chapter introduces the basics of logic programming and relates logic programming terminology to database terminology. It then defines the task of relational rule induction, the basic data mining task addressed by ILP systems, and presents some basic techniques for solving this task. It concludes with an overview of other relational data mining tasks addressed by ILP systems.