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Learning and Geometry: Computational Approaches pp 21–41Cite as

Pac Learning, Noise, and Geometry

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Part of the book series: Progress in Computer Science and Applied Logic ((PCS,volume 14))

Abstract

This paper describes the probably approximately correct model of concept learning, paying special attention to the case where instances are points in Euclidean n-space. The problem of learning from noisy training data is also studied.

Supported by NSF grant CCR-9108753. Email:sloan@eecs.uic.edu

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Author information

Authors and Affiliations

  1. Department of EE & Computer Science, University of Illinois at Chicago, 851 S. Morgan St. Room 1120, Chicago, IL, 60607-7053, USA

    Robert H. Sloan

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  1. Robert H. Sloan
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Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Maryland, 20742, College Park, MD, USA

    David W. Kueker

  2. Department of Computer Science, University of Maryland, 20742, College Park, MD, USA

    Carl H. Smith

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© 1996 Birkhäuser Boston

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Sloan, R.H. (1996). Pac Learning, Noise, and Geometry. In: Kueker, D.W., Smith, C.H. (eds) Learning and Geometry: Computational Approaches. Progress in Computer Science and Applied Logic, vol 14. Birkhäuser Boston. https://doi.org/10.1007/978-1-4612-4088-4_2

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  • DOI: https://doi.org/10.1007/978-1-4612-4088-4_2

  • Publisher Name: Birkhäuser Boston

  • Print ISBN: 978-1-4612-8646-2

  • Online ISBN: 978-1-4612-4088-4

  • eBook Packages: Springer Book Archive

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