Knowledge representation for inductive learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1638, Issue , 1999, Pages 160-167

  Flach, Peter A ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES; KNOWLEDGE REPRESENTATION; LEARNING SYSTEMS; SEMI-SUPERVISED LEARNING;

ATTRIBUTE VALUES; DECISION TREE LEARNERS; FIRST ORDER; HERBRAND INTERPRETATIONS; INDUCTIVE LEARNING;

LEARNING ALGORITHMS;

EID: 84947921682     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48747-6_15     Document Type: Conference Paper

References (8)

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6. P.A. Flach, C. Giraud-Carrier, and J.W. Lloyd. Strongly typed inductive concept learning. In D. Page, editor, Proceedings of the 8th International Conference on Inductive Logic Programming, volume 1446 of Lecture Notes in Artificial Intelligence, pages 185-194. Springer-Verlag, 1998.
7. P.A. Flach and N. Lachiche. IBC: a first-order Bayesian classifier. In S. Dzeroski and P.A. Flach, editors, Proceedings of the 9th International Workshop on Inductive Logic Programming (ILP’99), volume 1634 of Lecture Notes in Artificial Intelligence, pages 92-103. Springer-Verlag, 1999.
8. C. Rouveirol. Flattening and saturation: Two representation changes for generalization. Machine Learning, 14(2):219-232, 1994.