Communications of the ACM
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
Probability and plurality for aggregations of learning machines
Information and Computation
Probabilistic inductive inference
Journal of the ACM (JACM)
Probably approximate learning over classes of distributions
SIAM Journal on Computing
Efficient distribution-free learning of probabilistic concepts
Journal of Computer and System Sciences - Special issue: 31st IEEE conference on foundations of computer science, Oct. 22–24, 1990
On the learnability of discrete distributions
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
An introduction to computational learning theory
An introduction to computational learning theory
Investigations on measure-one identification of classes of languages
Information and Computation
Learning distributions by their density-levels - a paradigm for learning without a teacher
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
| Hi-index | 5.23 |
We investigate learning of classes of distributions over a discrete domain in a PAC context. We introduce two paradigms of PAC learning, namely absolute PAC learning, which is independent of the representation of the class of hypotheses, and PAC learning wrt the indexes, which heavily depends on such representations. We characterize non-computable learnability in both contexts. Then we investigate efficient learning strategies which are simulated by a polynomial-time Turing machine. One strategy is the frequentist one. According to this strategy, the learner conjectures a hypothesis which is as close as possible to the distribution given by the frequency relative to the examples. We characterize the classes of distributions which are absolutely PAC learnable by means of this strategy, and we relate frequentist learning wrt the indexes to the NP = RP problem. Finally, we present another strategy for learning wrt the indexes, namely learning by tests.