Inductive Logic Programming 12th International Conference, ILP 2002, Sydney, Australia, July 9-11, 2002. Revised Papers /
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...
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Other Authors: | , |
Format: | Electronic eBook |
Language: | English |
Published: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2003.
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Edition: | 1st ed. 2003. |
Series: | Lecture Notes in Artificial Intelligence ;
2583 |
Subjects: | |
Online Access: | Full Text via HEAL-Link |
Table of Contents:
- Contributed Papers
- Propositionalization for Clustering Symbolic Relational Descriptions
- Efficient and Effective Induction of First Order Decision Lists
- Learning with Feature Description Logics
- An Empirical Evaluation of Bagging in Inductive Logic Programming
- Kernels for Structured Data
- Experimental Comparison of Graph-Based Relational Concept Learning with Inductive Logic Programming Systems
- Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners
- Learnability of Description Logic Programs
- 1BC2: A True First-Order Bayesian Classifier
- RSD: Relational Subgroup Discovery through First-Order Feature Construction
- Mining Frequent Logical Sequences with SPIRIT-LoG
- Using Theory Completion to Learn a Robot Navigation Control Program
- Learning Structure and Parameters of Stochastic Logic Programs
- A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars
- Revision of First-Order Bayesian Classifiers
- The Applicability to ILP of Results Concerning the Ordering of Binomial Populations
- Compact Representation of Knowledge Bases in ILP
- A Polynomial Time Matching Algorithm of Structured Ordered Tree Patterns for Data Mining from Semistructured Data
- A Genetic Algorithms Approach to ILP
- Experimental Investigation of Pruning Methods for Relational Pattern Discovery
- Noise-Resistant Incremental Relational Learning Using Possible Worlds
- Lattice-Search Runtime Distributions May Be Heavy-Tailed
- Invited Talk Abstracts
- Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery.