Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Logic programming and databases
Logic programming and databases
Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning
Machine Learning - Special issue on multistrategy learning
Experience with a learning personal assistant
Communications of the ACM
Existence and nonexistence of complete refinement operators
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Machine Learning - Special issue on context sensitivity and concept drift
Equality and Domain Closure in First-Order Databases
Journal of the ACM (JACM)
Multistrategy Theory Revision: Induction and Abductionin INTHELEX
Machine Learning - Special issue on multistrategy learning
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Concept Formation and Knowledge Revision
Concept Formation and Knowledge Revision
Sampling from a moving window over streaming data
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Machine Learning - Special issue on context sensitivity and concept drift
Incremental Learning from Noisy Data
Machine Learning
Learning in Dynamically Changing Domains: Theory Revision and Context Dependence Issues
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Locally Finite, Proper and Complete Operators for Refining Datalog Programs
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL
LOPSTR '94/META '94 Proceedings of the 4th International Workshops on Logic Programming Synthesis and Transformation - Meta-Programming in Logic
Ideal Refinement of Datalog Programs
LOPSTR '95 Proceedings of the 5th International Workshop on Logic Programming Synthesis and Transformation
Traps and Pitfalls when Learning Logical Definitions from Relations
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Relational Learning by Imitation
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Metric-based stochastic conceptual clustering for ontologies
Information Systems
Metric-based stochastic conceptual clustering for ontologies
Information Systems
Induction of optimal semantic semi-distances for clausal knowledge bases
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
25 years of applications of logic programming in Italy
A 25-year perspective on logic programming
A multi-relational learning approach for knowledge extraction in in vitro fertilization domain
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
A multi-relational learning framework to support biomedical applications
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Active learning of relational action models
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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Real-world tasks often involve a continuous flow of new information that affects the learned theory, a situation that classical batch (one-step) learning systems are hardly suitable to handle. On the contrary, incremental (also called "on-line") techniques are able to deal with such a situation by exploiting refinement operators. In many cases deep knowledge about the world is not available: Either incomplete information is available at the time of initial theory generation, or the nature of the concepts evolves dynamically. The latter situation is the most difficult to handle since time evolution needs to be considered. This work presents a new approach to learning in presence of concept drift, and in particular a special version of the incremental system INTHELEX purposely designed to implement such a technique. Its behavior in this context has been checked and analyzed by running it on two different datasets.