Systems that learn: an introduction to learning theory for cognitive and computer scientists
Systems that learn: an introduction to learning theory for cognitive and computer scientists
Teachability in computational learning
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Machine learning: a theoretical approach
Machine learning: a theoretical approach
An analysis of stochastic shortest path problems
Mathematics of Operations Research
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
On specifying Boolean functions by labelled examples
Discrete Applied Mathematics
Witness sets for families of binary vectors
Journal of Combinatorial Theory Series A
Journal of Computer and System Sciences
Incremental learning from positive data
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)
Incremental concept learning for bounded data mining
Information and Computation
On the learnability of recursively enumerable languages from good examples
Theoretical Computer Science
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Machine Learning
Machine Learning
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
Inductive Inference from Good Examples
AII '89 Proceedings of the International Workshop on Analogical and Inductive Inference
Theoretical Computer Science - Special issue: Algorithmic learning theory
Teaching memoryless randomized learners without feedback
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Teaching learners with restricted mind changes
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
DNF are teachable in the average case
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
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
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 the learner's memory size and of the presence or absence of feedback. Moreover, in the new model the order in which examples are presented may influence the teaching process. The resulting models are related to Markov decision processes, and characterizations of optimal teachers for memoryless learners with feedback and for learners with infinite memory and feedback are shown. Furthermore, in the new model it is possible to investigate new aspects of teaching like teaching from positive data only or teaching with inconsistent teachers. Characterization theorems for teachability from positive data for both ordinary teachers and inconsistent teachers with and without feedback are provided.