The complexity of learning according to two models of a drifting environment

DOI10.1023/A:1007666507971zbMath0945.68083OpenAlexW4252260443MaRDI QIDQ1969322

Philip M. Long

Publication date: 16 March 2000

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1007666507971

zbMATH Keywords

agnostic learning

Mathematics Subject Classification ID

Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (6)

Efficient algorithms for learning functions with bounded variation ⋮ Learning from Non-iid Data: Fast Rates for the One-vs-All Multiclass Plug-in Classifiers ⋮ Ambiguous chance constrained problems and robust optimization ⋮ Performance guarantees for hierarchical clustering ⋮ Learning with a Drifting Target Concept ⋮ Exact lower bounds for the agnostic probably-approximately-correct (PAC) machine learning model

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