Computational limitations on learning from examples
Journal of the ACM (JACM)
Teachability in computational learning
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
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
Generalized teaching dimensions and the query complexity of learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Lower bounds on learning decision lists and trees
Information and Computation
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
Decision lists and related Boolean functions
Theoretical Computer Science
Machine Learning
Machine Learning
Machine Learning
Learning from Different Teachers
Machine Learning
Learning decision lists and trees with equivalence-queries
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
On Teaching and Learning Intersection-Closed Concept Classes
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Learning of R.E. Languages from Good Examples
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Learning binary relations, total orders, and read-once formulas
Learning binary relations, total orders, and read-once formulas
DNF are teachable in the average case
Machine Learning
Measuring teachability using variants of the teaching dimension
Theoretical Computer Science
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
Teaching memoryless randomized learners without feedback
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
DNF are teachable in the average case
COLT'06 Proceedings of the 19th annual conference on Learning Theory
| Hi-index | 0.00 |
We consider the Boolean concept classes of 2-term DNF and 1-decision lists which both have a teaching dimension exponential in the number n of variables. It is shown that both classes have an average teaching dimension linear in n. We also consider learners that always choose a simplest consistent hypothesis instead of an arbitrary consistent one. Both classes can be taught to these learners by efficient teaching algorithms using only a linear number of examples.