Learning from good and bad data
Learning from good and bad data
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
LIME: A System for Learning Relations
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
A Probabilistic Identification Result
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
A Noise Resistant Model Inference System
DS '99 Proceedings of the Second International Conference on Discovery Science
Learning Functions from Imperfect Positive Data
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Learning first-order Bayesian networks
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Learning first-order Horn clauses from web text
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Global learning of typed entailment rules
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Learning from noisy data using a non-covering ILP algorithm
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
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In order to rank the performance of machine learning algorithms, many researchers conduct experiments on benchmark data sets. Since most learning algorithms have domain-specific parameters, it is a popular custom to adapt these parameters to obtain a ...