Digital filters (3rd ed.)
Inferring decision trees using the minimum description length principle
Information and Computation
Identifying the grinding process by means of inductive machine learning
Computers in Industry - Special issue on IMS'91—Learning in IMS
Compression, significance and accuracy
ML92 Proceedings of the ninth international workshop on Machine learning
Employing linear regression in regression tree leaves
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Logical Definitions from Relations
Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Refining Numerical Constants in First Order Logic Theories
Machine Learning - Special issue on multistrategy learning
An introduction to inductive logic programming
Relational Data Mining
Inducing classification and regression trees in first order logic
Relational Data Mining
Relational data mining applications: an overview
Relational Data Mining
Internet resources on ILP for KDD
Relational Data Mining
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Relational Reinforcement Learning
Machine Learning
Toward effective knowledge acquisition with first-order logic induction
Journal of Computer Science and Technology
Challenges for Inductive Logic Programming
EPIA '99 Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Learning in Clausal Logic: A Perspective on Inductive Logic Programming
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part I
Modelling Learners of a Control Task with Inductive Logic Programming: A Case Study
MICAI '02 Proceedings of the Second Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
An Eager Regression Method Based on Best Feature Projections
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Using Machine Learning to Understand Operator's Skill
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Statistical Relational Learning for Document Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Quantitative pharmacophore models with inductive logic programming
Machine Learning
ReMauve: A Relational Model Tree Learner
Inductive Logic Programming
Ensembles of Multi-Objective Decision Trees
ECML '07 Proceedings of the 18th European conference on Machine Learning
Towards Machine Learning on the Semantic Web
Uncertainty Reasoning for the Semantic Web I
Automatic induction of bellman-error features for probabilistic planning
Journal of Artificial Intelligence Research
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
From inductive logic programming to relational data mining
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Learning predictive clustering rules
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Heuristic rule-based regression via dynamic reduction to classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Multi-target regression with rule ensembles
The Journal of Machine Learning Research
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We present a new approach, called First Order Regression (FOR),to handling numerical information in Inductive Logic Programming (ILP).FOR is a combination of ILP and numerical regression.First-order logic descriptions are induced to carve out those subspacesthat are amenable to numerical regression among real-valued variables.The program FORS is an implementation of this idea, where numericalregression is focused on a distinguished continuous argument of the targetpredicate. We show that this can be viewed as a generalisation of theusual ILP problem. Applications of FORS on several real-world datasets are described: the prediction of mutagenicity of chemicals, themodelling of liquid dynamics in a surge tank, predicting theroughness in steel grinding, finite element mesh design, andoperator‘s skill reconstruction in electric discharge machining. Acomparison of FORS‘ performance with previous results in thesedomains indicates that FORS is an effective tool for ILP applicationsthat involve numerical data.