Avoiding coding tricks by hyperrobust learning

Theoretical Computer Science

Volumn 284, Issue 1, 2002, Pages 161-180

  Ott, Matthias ^a   Stephan, Frank ^b  

^a UNIVERSITY OF KARLSRUHE   (Germany)

^b UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE ANALYSIS; IMAGE CODING; RECURSIVE FUNCTIONS; RELIABILITY; ROBUSTNESS (CONTROL SYSTEMS); TREES (MATHEMATICS);

ROBUST LEARNING;

LEARNING SYSTEMS;

EID: 0037031024     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3975(01)00086-X     Document Type: Conference Paper

References (20)

1. Two theorems on the limiting synthesis of functions
2. Towards a mathematical theory of inductive inference
3. Robust learning aided by context
4. Learning recursive functions from approximations
5. Comparison of identification criteria for machine inductive inference
6. Some decidability results on grammatical inference and complexity
7. Robust separations in inductive inference
8. On the power of learning robustly
9. Trees and learning
10. Some natural properties of strong-identification in inductive inference
11. Probablistic inductive inference
12. Probability and plurality for aggregations of learning machines
13. The power of pluralism for automatic program synthesis
14. On the nonboundability of total effective operators
15. On Bārzdiņš' conjecture