On compiling queries in recursive first-order databases
Journal of the ACM (JACM)
Automated Concept Acquisition in Noisy Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized subsumption and its applications to induction and redundancy
Artificial Intelligence
Foundations of deductive databases and logic programming
Foundations of deductive databases and logic programming
Towards a theory of declarative knowledge
Foundations of deductive databases and logic programming
Using genetic algorithms to learn disjunctive rules from examples
Proceedings of the seventh international conference (1990) on Machine learning
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Rigel: An Inductive Learning System
Machine Learning
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Learning nonrecursive definitions of relations with LINUS
EWSL-91 Proceedings of the European working session on learning on Machine learning
Induction as nonmonotonic inference
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Machine learning: an integrated framework and its applications
Machine learning: an integrated framework and its applications
The Utility of Knowledge in Inductive Learning
Machine Learning
Learning structured concepts using genetic algorithms
ML92 Proceedings of the ninth international workshop on Machine learning
A reinforcement learning-based architecture for fuzzy logic control
International Journal of Approximate Reasoning - Special issue on fuzzy logic and neural networks for pattern recognition and control
Automatic construction of second generation diagnostic expert systems
International Journal of Expert Systems
Multistrategy Learning and Theory Revision
Machine Learning - Special issue on multistrategy learning
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
Competition-Based Induction of Decision Models from Examples
Machine Learning - Special issue on genetic algorithms
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
Machine Learning - Special issue on evaluating and changing representation
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Evaluation and Selection of Biases in Machine Learning
Machine Learning - Special issue on bias evaluation and selection
Declarative Bias for Specific-to-General ILP Systems
Machine Learning - Special issue on bias evaluation and selection
Exploring the Power of Genetic Search in Learning Symbolic Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
An interference matching technique for inducing abstractions
Communications of the ACM
Algorithmic Program DeBugging
Learning Logical Definitions from Relations
Machine Learning
Learning Conjunctive Concepts in Structural Domains
Machine Learning
Learning Sequential Decision Rules Using Simulation Models and Competition
Machine Learning - Special issue on genetic algorithms
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
ENIGMA: A System That Learns Diagnostic Knowledge
IEEE Transactions on Knowledge and Data Engineering
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Generalized Convergence Models for Tournament- and (mu, lambda)-Selection
Proceedings of the 6th International Conference on Genetic Algorithms
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Multiple Predicate Learning with RTL
AI*IA '95 Proceedings of the 4th Congress of the Italian Association for Artificial Intelligence on Topics in Artificial Intelligence
Network Structuring and Training Using Rule-Based Knowledge
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Learning Simple Recursive Theories
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Search-intensive concept induction
Evolutionary Computation
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Refining Numerical Constants in First Order Logic Theories
Machine Learning - Special issue on multistrategy learning
Phase Transitions in Relational Learning
Machine Learning
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Abstraction and Phase Transitions in Relational Learning
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Design and Implementation of a Genetic-Based Algorithm for Data Mining
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Prediction Rule Discovery Based on Dynamic Bias Selection
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Integrated Architectures for Machine Learning
Machine Learning and Its Applications, Advanced Lectures
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
Relational concept learning by cooperative evolution
Journal of Experimental Algorithmics (JEA)
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This paper describes a representation framework that offers aunifying platform for alternative systems, which learn concepts inFirst Order Logics. The main aspects of this framework arediscussed. First of all, the separation between the hypothesislogical language (a version of the VL21 language) and therepresentation of data by means of a relational database ismotivated. Then, the functional layer between data and hypotheses,which makes the data accessible by the logical level through a set ofabstract properties is described. A novelty, in the hypothesisrepresentation language, is the introduction of the construct ofinternal disjunction; such a construct, first used by the AQ andInduce systems, is here made operational via a set of algorithms,capable to learn it, for both the discrete and the continuous-valuedattributes case. These algorithms are embedded in learning systems(SMART+, REGAL, SNAP, WHY, RTL) using different paradigms (symbolic,genetic or connectionist), thus realizing an effective integrationamong them; in fact, categorical and numerical attributes can behandled in a uniform way. In order to exemplify the effectivenessof the representation framework and of the multistrategy integration,the results obtained by the above systems in some application domainsare summarized.