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
Recursively enumerable sets and degrees
Recursively enumerable sets and degrees
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
International Workshop All '86 on Analogical and inductive inference
Prudence and other conditions on formal language learning
Information and Computation
On the role of search for learning
COLT '89 Proceedings of the second annual workshop on Computational learning theory
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Robust learning aided by context
Journal of Computer and System Sciences - Eleventh annual conference on computational learning theory&slash;Twelfth Annual IEEE conference on computational complexity
Journal of Computer and System Sciences
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Inductive Inference of Recursive Functions: Complexity Bounds
Baltic Computer Science, Selected Papers
Robust learning: rich and poor
Journal of Computer and System Sciences
Learning and extending sublanguages
Theoretical Computer Science
Learning and extending sublanguages
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
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Sublearning, a model for learning of subconcepts of a concept, is presented. Sublearning a class of total recursive functions informally means to learn all functions from that class together with all of their subfunctions. While in language learning it is known to be impossible to learn any infinite language together with all of its sublanguages, the situation changes for sublearning of functions. Several types of sublearning are defined and compared to each other as well as to other learning types. For example, in some cases, sublearning coincides with robust learning. Furthermore, whereas in usual function learning there are classes that cannot be learned consistently, all sublearnable classes of some natural types can be learned consistently. Moreover, the power of sublearning is characterized in several terms, thereby establishing a close connection to measurable classes and variants of this notion. As a consequence, there are rich classes which do not need any self-referential coding for sublearning them.