This is a provisional list of ILP 2002 accepted papers. Only the contact authors are listed.  This list will be superseded by the Conference Programme, to be posted by June 12.

Mining Frequent Logical Sequences with Spiritlog-Francois Jacquenet

Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners-David Jensen

Revision of First-Order Bayesian Classifiers-Kate Revoredo

A Polynomial Time Matching Algorithm of Structured Ordered Tree Patterns for Data Mining from Semistructured Data-Takayoshi Shoudai

The Applicability to ILP of Results Concerning the Ordering of Binomial Populations-Ashwin Srinivasan

Experimental Investigaton of Pruning Strategies for Relational Pattern Discovery-Irene Weber

Noise-resistant Incremental Relational Learning using Possible Worlds-James Westendorp

1BC2: a true first-order Bayesian classifier-Nicolas Lachiche

Lattice-Search Runtime Distributions May Be Heavy-Tailed-Filip Zelezny

Learning structure and parameters of Stochastic Logic Programs-Stephen Muggleton

Propositionalization for Clustering Symbolic Relational Descriptions-Mélanie Courtine

On the Learnability of Description Logic Programms-Jörg-Uwe Kietz

Experimental Comparison of Graph-Based Relational Concept Learning with Inductive Logic Programming Systems-Jesus Gonzalez

Macro-Operators in Multirelational Learning: a Search-Space Reduction Technique-Lourdes Pena

Using Theory Completion to learn a Robot Navigation Control Program-Steve Moyle

Scaling Boosting by Margin-Based Inclusion of Features and Relations-Susanne Hoche

Efficient and Effective Induction of First Order Decision Lists-Mary Elaine Califf

Kernels for Structured Data-Thomas Gaertner

Compact representation of knowledge bases in ILP-Jan Struyf

RSD: Relational subgroup discovery through first-order feature construction-Filip Zelezny

A Genetic Algorithms approach to ILP-Alireza Tamaddoni-Nezhad

An Empirical Evaluation of Bagging in Inductive Logic Programming-Ines Dutra

Learning Relations via Propositional Means-Chad Cumby

 Genetic Programming with Domain Knowledge for Machine Discovery-Michele Sebag