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
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Probability and plurality for aggregations of learning machines
Information and Computation
Probabilistic inductive inference
Journal of the ACM (JACM)
Prudence and other conditions on formal language learning
Information and Computation
Learning in the presence of inaccurate information
Theoretical Computer Science
Learning from Multiple Sources of Inaccurate Data
SIAM Journal on Computing
The synthesis of language learners
Information and Computation
The Power of Pluralism for Automatic Program Synthesis
Journal of the ACM (JACM)
Probabilistic inductive inference: a survey
Theoretical Computer Science
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
Three Decades of Team Learning
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
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Learning from streams is a process in which a group of learners separately obtain information about the target to be learned, but they can communicate with each other in order to learn the target. We are interested in machine models for learning from streams and study its learning power (as measured by the collection of learnable classes). We study how the power of learning from streams depends on the two parameters m and n, where n is the number of learners which track a single stream of input each and m is the number of learners (among the n learners) which have to find, in the limit, the right description of the target. We study for which combinations m, n and m′, n′ the following inclusion holds: Every class learnable from streams with parameters m, n is also learnable from streams with parameters m′, n′. For the learning of uniformly recursive classes, we get a full characterization which depends only on the ratio m/n ; but for general classes the picture is more complicated. Most of the noninclusions in team learning carry over to noninclusions with the same parameters in the case of learning from streams; but only few inclusions are preserved and some additional noninclusions hold. Besides this, we also relate learning from streams to various other closely related and well-studied forms of learning: iterative learning from text, learning from incomplete text and learning from noisy text.