Declarative bias for specific-to-general ILP systems-Reference-Cited by-同舟云学术

Declarative bias for specific-to-general ILP systems

Published:1995-07 Issue:1-2 Volume:20 Page:119-154
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Adé Hilde,de Raedt Luc,Bruynooghe Maurice

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/BF00993477.pdf

Reference42 articles.

1. Adé, H. & Bruynooghe, M. (1992). A comparative study of declarative and dynamically adjustable language bias in concept learning. InProceedings of the ML-92 Workshop on Biases in Inductive Learning.

2. Bergadano, F. (1993). Towards an inductive logic programming language. Technical Report ESPRIT project no. 6020 ILP Deliverable TO1, Computer Science Department, University of Torino.

3. Bergadano, F. & Gunetti, D. (1993). An interactive system to learn functional logic programs. InProceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1044-1049. Morgan Kaufmann.

4. Buntine, W. (1987). Induction of Horn-Clauses: methods and the plausible generalization algorithm.International Journal of Man-Machine Studies, 26:499?520.

5. Cameron-Jones, R.M. & Quinlan, J.R. (1993). Avoiding pitfalls when learning recursive theories. InProceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1050?1055. Morgan Kaufmann.

Cited by 22 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Human-Like Rule Learning from Images Using One-Shot Hypothesis Derivation;Inductive Logic Programming;2022

2. Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring;Frontiers in Robotics and AI;2020-07-31

3. Learning alternative ways of performing a task;Expert Systems with Applications;2020-06

4. Fast rule mining in ontological knowledge bases with AMIE $$+$$ +;The VLDB Journal;2015-07-22

5. AMIE;Proceedings of the 22nd international conference on World Wide Web - WWW '13;2013