General bounds on the number of examples needed for learning probabilistic concepts

DOI10.1006/jcss.1996.0019zbMath0851.68097OpenAlexW4232150823MaRDI QIDQ1916527

Hans Ulrich Simon

Publication date: 8 December 1996

Published in: Journal of Computer and System Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jcss.1996.0019

zbMATH Keywords

learning algorithms

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (8)

Some connections between learning and optimization ⋮ Efficient algorithms for learning functions with bounded variation ⋮ Learning distributions by their density levels: A paradigm for learning without a teacher ⋮ PAC-learning in the presence of one-sided classification~noise ⋮ Unnamed Item ⋮ Exact lower bounds for the agnostic probably-approximately-correct (PAC) machine learning model ⋮ Unnamed Item ⋮ Improved lower bounds for learning from noisy examples: An information-theoretic approach

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