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- catalog abstract "Inductive Logic Programming is a young and rapidly growing field combining machine learning and logic programming. This self-contained tutorial is the first theoretical introduction to ILP; it provides the reader with a rigorous and sufficiently broad basis for future research in the area. In the first part, a thorough treatment of first-order logic, resolution-based theorem proving, and logic programming is given. The second part introduces the main concepts of ILP and systematically develops the most important results on model inference, inverse resolution, unfolding, refinement operators, least generalizations, and ways to deal with background knowledge. Furthermore, the authors give an overview of PAC learning results in ILP and of some of the most relevant implemented systems.".
- catalog contributor b10525732.
- catalog contributor b10525733.
- catalog created "c1997.".
- catalog date "1997".
- catalog date "c1997.".
- catalog dateCopyrighted "c1997.".
- catalog description "I. Logic. 1. Propositional Logic. 2. First-Order Logic. 3. Normal Forms and Herbrand Models. 4. Resolution. 5. Subsumption Theorem and Refutation Completeness. 6. Linear and Input Resolution. 7. SLD-Resolution. 8. SLDNF-Resolution -- II. Inductive Logic Programming. 9. What Is Inductive Logic Programming? 10. The Framework for Model Inference. 11. Inverse Resolution. 12. Unfolding. 13. The Lattice and Cover Structure of Atoms. 14. The Subsumption Order. 15. The Implication Order. 16. Background Knowledge. 17. Refinement Operators. 18. PAC Learning. 19. Further Topics.".
- catalog description "Includes bibliographical references (p. [369]-390) and indexes.".
- catalog description "Inductive Logic Programming is a young and rapidly growing field combining machine learning and logic programming. This self-contained tutorial is the first theoretical introduction to ILP; it provides the reader with a rigorous and sufficiently broad basis for future research in the area. In the first part, a thorough treatment of first-order logic, resolution-based theorem proving, and logic programming is given. The second part introduces the main concepts of ILP and systematically develops the most important results on model inference, inverse resolution, unfolding, refinement operators, least generalizations, and ways to deal with background knowledge. Furthermore, the authors give an overview of PAC learning results in ILP and of some of the most relevant implemented systems.".
- catalog extent "xvii, 404 p. :".
- catalog identifier "3540629270 (Berlin : softcover : acid-free paper)".
- catalog isPartOf "Lecture notes in computer science ; 1228. Lecture notes in artificial intelligence".
- catalog isPartOf "Lecture notes in computer science ; 1228.".
- catalog isPartOf "Lecture notes in computer science. Lecture notes in artificial intelligence.".
- catalog issued "1997".
- catalog issued "c1997.".
- catalog language "eng".
- catalog publisher "Berlin ; New York : Springer,".
- catalog subject "005.1/15 21".
- catalog subject "Artificial intelligence.".
- catalog subject "Computer science.".
- catalog subject "Induction (Logic)".
- catalog subject "Logic programming.".
- catalog subject "Machine learning.".
- catalog subject "QA76.63 .N54 1997".
- catalog tableOfContents "I. Logic. 1. Propositional Logic. 2. First-Order Logic. 3. Normal Forms and Herbrand Models. 4. Resolution. 5. Subsumption Theorem and Refutation Completeness. 6. Linear and Input Resolution. 7. SLD-Resolution. 8. SLDNF-Resolution -- II. Inductive Logic Programming. 9. What Is Inductive Logic Programming? 10. The Framework for Model Inference. 11. Inverse Resolution. 12. Unfolding. 13. The Lattice and Cover Structure of Atoms. 14. The Subsumption Order. 15. The Implication Order. 16. Background Knowledge. 17. Refinement Operators. 18. PAC Learning. 19. Further Topics.".
- catalog title "Foundations of inductive logic programming / S.-H. Nienhuys-Cheng, Ronald de Wolf.".
- catalog type "text".