Skip to main content
SpringerLink
Log in

The Mathematics of Learning: Dealing with Data *

  • Chapter
  • First Online:
Foundations and Advances in Data Mining

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 180))

Abstract

Learning is key to developing systems tailored to a broad range of data analysis and information extraction tasks. We outline the mathematical foundations of learning theory and describe a key algorithm of it.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Author information

Authors and Affiliations

  1. Artificial Intelligence Lab, BCS, MIT, CBCL, McGovern Institute, Cambridge, MA, UK

    T. Poggio

  2. Toyota Technological Institute at Chicago and Professor in the Graduate School, University of California, Berkeley

    S. Smale

Authors
  1. T. Poggio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Smale
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Wesley Chu Tsau Young Lin

Rights and permissions

Reprints and permissions

About this chapter

Cite this chapter

Poggio, T., Smale, S. The Mathematics of Learning: Dealing with Data *. In: Chu, W., Young Lin, T. (eds) Foundations and Advances in Data Mining. Studies in Fuzziness and Soft Computing, vol 180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11362197_1

Download citation

  • DOI: https://doi.org/10.1007/11362197_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25057-9

  • Online ISBN: 978-3-540-32393-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation