The Twelfth International Conference on Inductive Logic Programming was held in Sydney, Australia, July 9–11, 2002. The conference was colocated with two other events, the Nineteenth International Conference on Machine Learning (ICML2002) and the Fifteenth Annual Conference on Computational Learning Theory (COLT2002). Startedin1991,InductiveLogicProgrammingistheleadingannualforumfor researchers working in Inductive Logic Programming and Relational Learning. Continuing a series of international conferences devoted to Inductive Logic Programming and Relational Learning, ILP 2002 was the central event in 2002 for researchers interested in learning relational knowledge from examples. The Program Committee, following a resolution of the Community Me- ing in Strasbourg in September 2001, took upon itself the issue of the possible change of the name of the conference. Following an extended e-mail discussion, a number of proposed names were subjected to a vote. In the ?rst stage of the vote, two names were retained for the second vote. The two names were: Ind- tive Logic Programming, and Relational Learning. It had been decided that a 60% vote would be needed to change the name; the result of the vote was 57% in favor of the name Relational Learning. Consequently, the name Inductive Logic Programming was kept.
Author(s): Isabelle Bournaud, Mélanie Courtine, Zucker Jean-Daniel (auth.), Stan Matwin, Claude Sammut (eds.)
Series: Lecture Notes in Computer Science 2583 : Lecture Notes in Artificial Intelligence
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2003
Language: English
Pages: 358
Tags: Artificial Intelligence (incl. Robotics); Computer Science, general; Programming Techniques; Algorithm Analysis and Problem Complexity; Mathematical Logic and Formal Languages; Database Management
Propositionalization for Clustering Symbolic Relational Descriptions....Pages 1-16
Efficient and Effective Induction of First Order Decision Lists....Pages 17-31
Learning with Feature Description Logics....Pages 32-47
An Empirical Evaluation of Bagging in Inductive Logic Programming....Pages 48-65
Kernels for Structured Data....Pages 66-83
Experimental Comparison of Graph-Based Relational Concept Learning with Inductive Logic Programming Systems....Pages 84-100
Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners....Pages 101-116
Learnability of Description Logic Programs....Pages 117-132
1BC2: A True First-Order Bayesian Classifier....Pages 133-148
RSD: Relational Subgroup Discovery through First-Order Feature Construction....Pages 149-165
Mining Frequent Logical Sequences with SPIRIT-LoG....Pages 166-182
Using Theory Completion to Learn a Robot Navigation Control Program....Pages 182-197
Learning Structure and Parameters of Stochastic Logic Programs....Pages 198-206
A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars....Pages 207-222
Revision of First-Order Bayesian Classifiers....Pages 223-237
The Applicability to ILP of Results Concerning the Ordering of Binomial Populations....Pages 238-253
Compact Representation of Knowledge Bases in ILP....Pages 254-269
A Polynomial Time Matching Algorithm of Structured Ordered Tree Patterns for Data Mining from Semistructured Data....Pages 270-284
A Genetic Algorithms Approach to ILP....Pages 285-300
Experimental Investigation of Pruning Methods for Relational Pattern Discovery....Pages 301-316
Noise-Resistant Incremental Relational Learning Using Possible Worlds....Pages 317-332
Lattice-Search Runtime Distributions May Be Heavy-Tailed....Pages 333-345
Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery....Pages 346-349
