Communications of the ACM
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Guiding induction with domain theories
Machine learning
The Utility of Knowledge in Inductive Learning
Machine Learning
Bayesian inductive logic programming
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Logical Definitions from Relations
Machine Learning
Inductive Logic Programming for Natural Language Processing
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Search-intensive concept induction
Evolutionary Computation
Further experimental evidence against the utility of Occam's razor
Journal of Artificial Intelligence Research
Rule induction and instance-based learning a unified approach
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
An extended transformation approach to inductive logic programming
ACM Transactions on Computational Logic (TOCL) - Special issue devoted to Robert A. Kowalski
Propositionalization approaches to relational data mining
Relational Data Mining
A Study of Two Sampling Methods for Analyzing Large Datasets with ILP
Data Mining and Knowledge Discovery
Phase Transitions in Relational Learning
Machine Learning
Relational Instance-Based Learning with Lists and Terms
Machine Learning
A Framework for Learning Rules from Multiple Instance Data
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
CL '00 Proceedings of the First International Conference on Computational Logic
Scalability Issues in Inductive Logic Programming
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
A Note on Two Simple Transformations for Improving the Efficiency of an ILP System
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Learning Logic Programs with Neural Networks
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Application of Pruning Techniques for Propositional Learning to Progol
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
A Genetic Algorithm for Propositionalization
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Relational Learning: Hard Problems and Phase Transitions
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Resampling vs Reweighting in Boosting a Relational Weak Learner
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
A Dynamic Approach to Dimensionality Reduction in Relational Learning
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Applying Preference Biases to Conjunctive and Disjunctive Version Spaces
AIMSA '00 Proceedings of the 9th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Filtering Multi-Instance Problems to Reduce Dimensionality in Relational Learning
Journal of Intelligent Information Systems
Relational concept learning by cooperative evolution
Journal of Experimental Algorithmics (JEA)
Ilp: a short look back and a longer look forward
The Journal of Machine Learning Research
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
Scalability and efficiency in multi-relational data mining
ACM SIGKDD Explorations Newsletter
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Adapting RBF Neural Networks to Multi-Instance Learning
Neural Processing Letters
Propositionalization-based relational subgroup discovery with RSD
Machine Learning
Evolutionary concept learning in first order logic: an overview
AI Communications
Relational IBL in classical music
Machine Learning
Fast estimation of first-order clause coverage through randomization and maximum likelihood
Proceedings of the 25th international conference on Machine learning
Approximate Reasoning for Efficient Anytime Induction from Relational Knowledge Bases
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
A Restarted Strategy for Efficient Subsumption Testing
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Parallel ILP for distributed-memory architectures
Machine Learning
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Constructive induction: a version space-based approach
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Learning with feature description logics
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
An empirical evaluation of bagging in inductive logic programming
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
The applicability to ILP of results concerning the ordering of binomial populations
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Stochastic propositionalization for efficient multi-relational learning
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
When does it pay off to use sophisticated entailment engines in ILP?
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
On applying tabling to inductive logic programming
ECML'05 Proceedings of the 16th European conference on Machine Learning
A study of applying dimensionality reduction to restrict the size of a hypothesis space
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Efficient sampling in relational feature spaces
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Compile the Hypothesis Space: Do it Once, Use it Often
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
A Restarted Strategy for Efficient Subsumption Testing
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Hi-index | 0.00 |
Learning in first-order logic (FOL) languages suffers from a specific difficulty: both induction and classification are potentially exponential in the size of hypotheses. This difficulty is usually dealt with by limiting the size of hypotheses, via either syntactic restrictions or search strategies. This paper is concerned with polynomial induction and use of FOL hypotheses with no size restrictions. This is done via stochastic matching: instead of exhaustively exploring the set of matchings between any example and any short candidate hypothesis, one stochastically explores the set of matchings between any example and any candidate hypothesis. The user sets the number of matching samples to consider and thereby controls the cost of induction and classification. One advantage of this heuristic is to allow for resource-bounded learning, without any a priori knowledge about the problem domain. Experiments on a real-world problem pertaining to organic chemistry fully demonstrate the potentialities of the approach regarding both predictive accuracy and computational cost.