Proceedings of the sixth international workshop on Machine learning
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Employing linear regression in regression tree leaves
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
FOSSIL: a robust relational learner
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Pac-learning nondeterminate clauses
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Learning Logical Definitions from Relations
Machine Learning
Handling Real Numbers in ILP: A Step Towards Better Behavioural Clones (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Rule-based machine learning methods for functional prediction
Journal of Artificial Intelligence Research
Learning structural decision trees from examples
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Covering vs divide-and-conquer for top-down induction of logic programs
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
An introduction to inductive logic programming
Relational Data Mining
Inducing classification and regression trees in first order logic
Relational Data Mining
How to upgrade propositional learners to first order logic: case study
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Relational reinforcement learning
Mutli-agents systems and applications
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
Relational Reinforcement Learning
Machine Learning
Multi-Relational Data Mining, Using UML for ILP
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Relational Reinforcement Learning
EASSS '01 Selected Tutorial Papers from the 9th ECCAI Advanced Course ACAI 2001 and Agent Link's 3rd European Agent Systems Summer School on Multi-Agent Systems and Applications
How to Upgrade Propositional Learners to First Order Logic: A Case Study
Machine Learning and Its Applications, Advanced Lectures
Experiments in Predicting Biodegradability
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Efficient Cross-Validation in ILP
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Prediction of Ordinal Classes Using Regression Trees
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Data mining tasks and methods: Rule discovery: inductive logic programming approaches
Handbook of data mining and knowledge discovery
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Integrating Guidance into Relational Reinforcement Learning
Machine Learning
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
ReMauve: A Relational Model Tree Learner
Inductive Logic Programming
Learning MDP Action Models Via Discrete Mixture Trees
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System
Learning Classifier Systems
Proceedings of the 2005 conference on Multi-Relational Data Mining
kFOIL: learning simple relational kernels
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
Top-down induction of first-order logical decision trees
Artificial Intelligence
Learning with kernels and logical representations
Probabilistic inductive logic programming
Computing graph-based lattices from smallest projections
KONT'07/KPP'07 Proceedings of the First international conference on Knowledge processing and data analysis
From inductive logic programming to relational data mining
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Learning closed sets of labeled graphs for chemical applications
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Predicate selection for structural decision trees
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
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In many real-world domains the task of machine learning algorithms is to learn a theory for predicting numerical values. In particular several standard test domains used in Inductive Logic Programming (ILP) are concerned with predicting numerical values from examples and relational and mostly non-determinate background knowledge. However, so far no ILP algorithm except one can predict numbers and cope with nondeterminate background knowledge. (The only exception is a covering algorithm called FORS.) In this paper we present Structural Regression Trees (SRT), a new algorithm which can be applied to the above class of problems. SRT integrates the statistical method of regression trees into ILP. It constructs a tree containing a literal (an atomic formula or its negation) or a conjunction of literals in each node, and assigns a numerical value to each leaf. SRT provides more comprehensible results than purely statistical methods, and can be applied to a class of problems most other ILP systems cannot handle. Experiments in several real-world domains demonstrate that the approach is competitive with existing methods, indicating that the advantages are not at the expense of predictive accuracy.