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
A connotational theory of program structure
A connotational theory of program structure
Monotonic and non-monotonic inductive inference
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Language learning from texts: mindchanges, limited memory, and monotonicity
Information and Computation
On the impact of forgetting on learning machines
Journal of the ACM (JACM)
Incremental learning from positive data
Journal of Computer and System Sciences
Incremental concept learning for bounded data mining
Information and Computation
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
ICALP '00 Proceedings of the 27th International Colloquium on Automata, Languages and Programming
A Thesis in Inductive Inference
Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic
Non U-shaped vacillatory and team learning
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Variations on u-shaped learning
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Iterative learning from positive data and negative counterexamples
Information and Computation
Information and Computation
Resource Restricted Computability Theoretic Learning: Illustrative Topics and Problems
CiE '07 Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World
Parallelism Increases Iterative Learning Power
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
U-shaped, iterative, and iterative-with-counter learning
COLT'07 Proceedings of the 20th annual conference on Learning theory
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U-shaped learning is a learning behaviour in which the learner first learns something, then unlearns it and finally relearns it. Such a behaviour, observed by psychologists, for example, in the learning of past-tenses of English verbs, has been widely discussed among psychologists and cognitive scientists as a fundamental example of the non-monotonicity of learning. Previous theory literature has studied whether or not U-shaped learning, in the context of Gold’s formal model of learning languages from positive data, is necessary for learning some tasks. It is clear that human learning involves memory limitations. In the present paper we consider, then, this question of the necessity of U-shaped learning for some learning models featuring memory limitations. Our results show that the question of the necessity of U-shaped learning in this memory-limited setting depends on delicate tradeoffs between the learner’s ability to remember its own previous conjecture, to store some values in its long-term memory, to make queries about whether or not items occur in previously seen data and on the learner’s choice of hypothesis space.