Measure, category and learning theory

DOI10.1007/3-540-60084-1_105zbMath1412.68093OpenAlexW1545761782MaRDI QIDQ4645211

Stuart A. Kurtz, Carl H. Smith, Martin Kummer, Frank Stephan, William I. Gasarch, Lance J. Fortnow, Rūsiņš Freivalds

Publication date: 10 January 2019

Published in: Automata, Languages and Programming (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/3-540-60084-1_105

zbMATH Keywords

inductive inferenceteam learningrecursive functioninitial segmentrecursive tree

Mathematics Subject Classification ID

Computational learning theory (68Q32) Recursive functions and relations, subrecursive hierarchies (03D20) Theory of numerations, effectively presented structures (03D45)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Comparison of identification criteria for machine inductive inference
• Probability and plurality for aggregations of learning machines
• Almost everywhere high nonuniform complexity
• Breaking the probability \({1\over 2}\) barrier in FIN-type learning
• Aggregating inductive expertise
• Probabilistic inductive inference
• THE BANACH-MAZUR GAME
• The Power of Pluralism for Automatic Program Synthesis
• Toward a mathematical theory of inductive inference
• Language identification in the limit

This page was built for publication: Measure, category and learning theory