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
Journal of Computer and System Sciences
Journal of Computer and System Sciences
A model of interactive teaching
Journal of Computer and System Sciences - special issue on complexity theory
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Teachers, learners and black boxes
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
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
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Machine Learning
Machine Learning
Learning from Different Teachers
Machine Learning
Learning of R.E. Languages from Good Examples
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Theoretical Computer Science - Special issue: Algorithmic learning theory
Recent Developments in Algorithmic Teaching
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
Teaching memoryless randomized learners without feedback
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Massive online teaching to bounded learners
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
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The present paper introduces a new model for teaching randomized learners. Our new model, though based on the classical teaching dimension model, allows to study the influence of various parameters such as the learner’s memory size, its ability to provide or to not provide feedback, and the influence of the order in which examples are presented. Furthermore, within the new model it is possible to investigate new aspects of teaching like teaching from positive data only or teaching with inconsistent teachers. Furthermore, we provide characterization theorems for teachability from positive data for both ordinary teachers and inconsistent teachers with and without feedback.