Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Synthesizing inductive expertise
Information and Computation
On uniform learnability of language families
Information Processing Letters
On the intrinsic complexity of learning
Information and Computation
The intrinsic complexity of language identification
Journal of Computer and System Sciences
On the intrinsic complexity of learning recursive functions
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
The synthesis of language learners
Information and Computation
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Language Learning From Texts: Degrees of Instrinsic Complexity and Their Characterizations
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
On the intrinsic complexity of learning recursive functions
Information and Computation
Learning recursive functions: A survey
Theoretical Computer Science
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Inductive inference is concerned with algorithmic learning of recursive functions. In the model of learning in the limit a learner successful for a class of recursive functions must eventually find a program for any function in the class from a gradually growing sequence of its values. This approach is generalised in uniform learning, where the problem of synthesising a successful learner for a class of functions from a description of this class is considered.A common reduction-based approach for comparing the complexity of learning problems in inductive inference is intrinsic complexity. Informally, if a learning problem (a class of recursive functions) A is reducible to a learning problem (a class of recursive functions) B, then a solution for B can be transformed into a solution for A. In the context of intrinsic complexity, reducibility between two classes is expressed via recursive operators transforming target functions in one direction and sequences of corresponding hypotheses in the other direction.The present paper is concerned with intrinsic complexity of uniform learning. The relevant notions are adapted and illustrated by several examples. Characterisations of complete classes finally allow for various insightful conclusions. The connection to intrinsic complexity of non-uniform learning is revealed within several analogies concerning first the structure of complete classes and second the general interpretation of the notion of intrinsic complexity.