New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
An incremental concept formation approach for learning from databases
Theoretical Computer Science - Special issue on formal methods in databases and software engineering
Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
A framework for incremental learning of logic programs
Theoretical Computer Science - Special issue on algorithmic learning theory
Inductive logic programming: issues, results and the challenge of learning language in logic
Artificial Intelligence - Special issue on applications of artificial intelligence
Data on the Web: from relations to semistructured data and XML
Data on the Web: from relations to semistructured data and XML
Inductive Logic Programming: From Machine Learning to Software Engineering
Inductive Logic Programming: From Machine Learning to Software Engineering
Automated Software Engineering
On the Proper Definition of Minimality in Specialization and Theory Revision
ECML '93 Proceedings of the European Conference on Machine Learning
An integrated framework for the diagnosis and correction of rule-based programs
Theoretical Computer Science
Transformation and debugging of functional logic programs
A 25-year perspective on logic programming
An integrated distance for atoms
FLOPS'10 Proceedings of the 10th international conference on Functional and Logic Programming
Learning with configurable operators and RL-based heuristics
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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In this work, we consider the extension of the Inductive Functional Logic Programming (IFLP) framework in order to learn functions in an incremental way. In general, incremental learning is necessary when the number of examples is infinite, very large or presented one by one. We have performed this extension in the FLIP system, an implementation of the IFLP framework. Several examples of programs which have been induced indicate that our extension pays off in practice. An experimental study of some parameters which affect this efficiency is performed and some applications for programming practice are illustrated, especially small classification problems and data-mining of semi-structured data.