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
Reinforcement learning with hidden states
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Language learning from texts: mindchanges, limited memory, and monotonicity
Information and Computation
Incremental learning from positive data
Journal of Computer and System Sciences
Incremental concept learning for bounded data mining
Information and Computation
Machine Learning
Introduction to Automata Theory, Languages and Computability
Introduction to Automata Theory, Languages and Computability
Neural Computation
Results on memory-limited U-shaped learning
Information and Computation
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
Learning in Friedberg Numberings
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Learning with ordinal-bounded memory from positive data
Journal of Computer and System Sciences
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In the inductive inference framework of learning in the limit, a variation of the bounded example memory (Bem) language learning model is considered. Intuitively, the new model constrains the learner's memory not only in how muchdata may be retained, but also in how longthat data may be retained. More specifically, the model requires that, if a learner commits an example xto memory in some stage of the learning process, then there is some subsequent stage for which xno longerappears in the learner's memory. This model is called temporary example memory(Tem) learning. In some sense, it captures the idea that memories fade.Many interesting results concerning the Tem-learning model are presented. For example, there exists a class of languages that can be identified by memorizing k+ 1 examples in the Temsense, but that cannotbe identified by memorizing kexamples in the Bemsense. On the other hand, there exists a class of languages that can be identified by memorizing just1 examplein the Bemsense, but that cannotbe identified by memorizing any number of examplesin the Temsense. (The proof of this latter result involves an infinitary self-reference argument.) Results are also presented concerning the special cases of: learning indexableclasses of languages, and learning (arbitrary) classes of infinitelanguages.