A decision-theoretic extension of stochastic complexity and its applications to learning

DOI10.1109/18.681319zbMath0935.94005OpenAlexW2105154442MaRDI QIDQ4701114

Kenji Yamanishi

Publication date: 21 November 1999

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/18.681319

zbMATH Keywords

predictionupper boundslearningminimum description lengthstatistical riskstochastic complexitybatch-learning

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05) Information theory (general) (94A15) Statistical aspects of information-theoretic topics (62B10)

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