Avoiding coding tricks by hyperrobust learning

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

Volumn 1572, Issue , 1999, Pages 183-197

  Ott, Matthias ^a   Stephan, Frank ^b  

^a UNIVERSITY OF KARLSRUHE   (Germany)

^b UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

LEARNABILITY; LEARNING CRITERION;

COMPUTATION THEORY;

EID: 84947779228     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-49097-3_15     Document Type: Conference Paper

References (17)

1. Janis Bārzdiņš. Two theorems on the limiting synthesis of functions. In Theory of Algorithms and Programs, Latvian State University, Riga, 210: 82-88, 1974.
2. Leonard Blum and Manuel Blum. Towards a mathematical theory of inductive inference. Information and Control, 28: 125-155, 1975.
3. John Case, Sanjay Jain, Matthias Ott, Arun Sharma and Frank Stephan. Robust learning aided by context. In Proceedings of Eleventh Annual Conference on Computational Learning Theory (COLT), pages 44-55, ACM Press, New York, 1998.
4. John Case, Susanne Kaufmann, Efim Kinber and Martin Kummer. Learning recursive functions from approximations. Journal of Computer and System Sciences, 55: 183-196, 1997.
5. John Case and Carl Smith. Comparison of identification criteria for machine inductive inference. Theoretical Computer Science, 25: 193-220, 1983.
6. Jerome Feldmann. Some decidability results on grammatical inference and complexity. Information and Control, 20: 244-262, 1972.
7. Marc Fulk. Robust separations in inductive inference. In Proceedings of the 31st Annual Symposium on Foundations of Computer Science (FOCS), pages 405-410, St. Louis, Missouri, 1990.
8. Sanjay Jain, Carl Smith and Rolf Wiehagen. On the power of learning robustly. In Proceedings of Eleventh Annual Conference on Computational Learning Theory (COLT), pages 187-197, ACM Press, New York, 1998.
9. Wolfgang Merkle and Frank Stephan. Trees and learning. Proceedings of the Ninth Annual Conference on Computational Learning Theory (COLT), pages 270-279, ACM Press, New York, 1996.
10. Eliana Minicozzi. Some natural properties of strong-identification in inductive inference. Theoretical Computer Science, 2: 345-360, 1976.
11. Piergiorgio Odifreddi. Classical Recursion Theory. North-Holland, Amsterdam, 1989.
12. Daniel Osherson, Michael Stob and Scott Weinstein. Systems that Learn. MIT Press, Cambridge, Massachusetts, 1986.
13. Lenny Pitt. Probablistic inductive inference. Journal of the Association of Computing Machinery, 36: 383-433, 1989.
14. Lenny Pitt and Carl Smith. Probability and plurality for aggregations of learning machines. Information and Computation, 77: 77-92, 1998.
15. Karlis Podnieks. Comparing various concepts of function prediction, Part 1. Theory of Algorithms and Programs, Latvian State University, Riga, 210: 68-81, 1974.
16. Carl Smith. The power of pluralism for automatic program synthesis Journal of the Association of Computing Machinery, 29: 1144-1165, 1982.
17. Thomas Zeugmann. On Bārzdiņs' conjecture. In K. P. Jantke, editor, Proceedings of the International Workshop on Analogical and Inductive Inference (AII'86), volume 265 of LNCS, pages 220-227. Springer, 1986.