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 study of inductive inference machines
A study of inductive inference machines
Recursively enumerable sets and degrees
Recursively enumerable sets and degrees
Introduction to mathematical logic (3rd ed.)
Introduction to mathematical logic (3rd ed.)
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
A connotational theory of program structure
A connotational theory of program structure
Strong separation of learning classes
Journal of Experimental & Theoretical Artificial Intelligence
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
An Introduction to the General Theory of Algorithms
An Introduction to the General Theory of Algorithms
Machine Learning of Higher Order Programs
TVER '92 Proceedings of the Second International Symposium on Logical Foundations of Computer Science
The independence of control structures in abstract programming systems
The independence of control structures in abstract programming systems
Tradeoffs in machine inductive inference
Tradeoffs in machine inductive inference
Prefix-Like complexities and computability in the limit
CiE'06 Proceedings of the Second conference on Computability in Europe: logical Approaches to Computational Barriers
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Machine learning of limit programs (i.e., programs allowed finitely many mind changes about their legitimate outputs) for computable functions is studied. Learning of iterated limit programs is also studied. To partially motivate these studies, it is shown that, in some cases, interesting global properties of computable functions can be proved from suitable (n+1)-iterated limit programs for them which can not be proved from any n-iterated limit programs for them. It is shown that learning power is increased when (n+1)-iterated limit programs rather than n-iterated limit programs are to be learned. Many tradeoff results are obtained regarding learning power, number (possibly zero) of limits taken, program size constraints, and number of errors tolerated in final programs learned.