Teaching memoryless randomized learners without feedback

Authors:
Frank J. Balbach;Thomas Zeugmann
Affiliations:
Institut für Theoretische Informatik, Universität zu Lübeck, Lübeck, Germany;Division of Computer Science, Hokkaido University, Sapporo, Japan
Venue:
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Year:
2006

Citing 18
Cited 2

Teachability in computational learning

New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
On exact specification by examples

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A computational model of teaching

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
On the power of inductive inference from good examples

Theoretical Computer Science
Learning binary relations and total orders

SIAM Journal on Computing
On the complexity of teaching

Journal of Computer and System Sciences
Teaching a smarter learner

Journal of Computer and System Sciences
A model of interactive teaching

Journal of Computer and System Sciences - special issue on complexity theory
Teachers, learners and black boxes

COLT '97 Proceedings of the tenth annual conference on Computational learning theory
On the undecidability of probabilistic planning and infinite-horizon partially observable Markov decision problems

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
On the learnability of recursively enumerable languages from good examples

Theoretical Computer Science
Dynamic Programming and Optimal Control

Dynamic Programming and Optimal Control
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Learning from Different Teachers

Machine Learning
On Teaching and Learning Intersection-Closed Concept Classes

EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
DNF are teachable in the average case

COLT'06 Proceedings of the 19th annual conference on Learning Theory
Teaching randomized learners

COLT'06 Proceedings of the 19th annual conference on Learning Theory
Teaching classes with high teaching dimension using few examples

COLT'05 Proceedings of the 18th annual conference on Learning Theory

Recent Developments in Algorithmic Teaching

LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
Teaching randomized learners with feedback

Information and Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The present paper mainly studies the expected teaching time of memoryless randomized learners without feedback. First, a characterization of optimal randomized learners is provided and, based on it, optimal teaching teaching times for certain classes are established. Second, the problem of determining the optimal teaching time is shown to be -hard. Third, an algorithm for approximating the optimal teaching time is given. Finally, two heuristics for teaching are studied, i.e., cyclic teachers and greedy teachers.