Computability
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
Prudence and other conditions on formal language learning
Information and Computation
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Composition is almost (but not quite) as good as s–1–1
Theoretical Computer Science
Computability, complexity, and languages (2nd ed.): fundamentals of theoretical computer science
Computability, complexity, and languages (2nd ed.): fundamentals of theoretical computer science
Co-learning of total recursive functions
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Subrecursive programming systems: complexity & succinctness
Subrecursive programming systems: complexity & succinctness
On the intrinsic complexity of learning
Information and Computation
The intrinsic complexity of language identification
Journal of Computer and System Sciences
A Machine-Independent Theory of the Complexity of Recursive Functions
Journal of the ACM (JACM)
Denotational Semantics: The Scott-Strachey Approach to Programming Language Theory
Denotational Semantics: The Scott-Strachey Approach to Programming Language Theory
Anatomy of LISP
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Characterization Problems in the Theory of Inductive Inference
Proceedings of the Fifth Colloquium on Automata, Languages and Programming
A Guided Tour Across the Boundaries of Learning Recursive Languages
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
The independence of control structures in abstract programming systems
The independence of control structures in abstract programming systems
The Calculi of Lambda Conversion. (AM-6) (Annals of Mathematics Studies)
The Calculi of Lambda Conversion. (AM-6) (Annals of Mathematics Studies)
Characterizing Programming Systems Allowing Program Self-reference
CiE '07 Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World
Hypothesis Spaces for Learning
LATA '09 Proceedings of the 3rd International Conference on Language and Automata Theory and Applications
Index Sets and Universal Numberings
CiE '09 Proceedings of the 5th Conference on Computability in Europe: Mathematical Theory and Computational Practice
Numberings optimal for learning
Journal of Computer and System Sciences
Hypothesis spaces for learning
Information and Computation
Index sets and universal numberings
Journal of Computer and System Sciences
Properties Complementary to Program Self-Reference
Fundamenta Informaticae
Properties complementary to program self-reference
MFCS'07 Proceedings of the 32nd international conference on Mathematical Foundations of Computer Science
| Hi-index | 5.23 |
In any learnability setting, hypotheses are conjectured from some hypothesis space. Studied herein are the influence on learnability of the presence or absence of certain control structures in the hypothesis space. First presented are control structure characterizations of some rather specific but illustrative learnability results. The presence of these control structures is thereby shown essential to maintain full learning power. Then presented are the main theorems. Each of these non-trivially characterizes the invariance of a learning class over hypothesis space V and the presence of a particular projection control structure, called proj, in V as: V has suitable instances of all denotational control structures. In a sense, then, proj epitomizes the control structures whose presence need not help and whose absence need not hinder learning power.