Skip to main content
SpringerLink
Log in

Classification

  • Reference work entry
  • First Online:
Book cover Encyclopedia of Database Systems

  • 47 Accesses

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 6,499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and regression trees. Pacific Grove: Wadsworth; 1984.

    MATH  Google Scholar 

  2. Bush RR, Mosteller F. Stochastic models for learning. New York: Wiley; 1955.

    Book  MATH  Google Scholar 

  3. Holte RC. Very simple classification rules perform well on most commonly used datasets. Mach Learn. 1993;11:63–91.

    Article  MATH  Google Scholar 

  4. Kononebko I. ID3, sequential Bayes, naïve Bayes and Bayesian neural networks. In: Proceedings of the 4th European Working Session on Learning; 1989. p. 91–8.

    Google Scholar 

  5. Maron ME, Kuhns JL. On relevance, probabilistic indexing and information retrieval. J ACM. 1960;7(3):216–44.

    Article  Google Scholar 

  6. Minsky ML, Papert S. Perceptrons. Cambridge: MIT Press; 1969.

    MATH  Google Scholar 

  7. Nilsson NJ. Learning machines. New York: McGraw-Hill; 1965.

    MATH  Google Scholar 

  8. Quinlan JR. Induction of decision trees. Mach Learn. 1986;1(1):81–106.

    Google Scholar 

  9. Quinlan JR. C4.5: programs for machine learning. San Francisco: Morgan Kaufmann; 1993.

    Google Scholar 

  10. Rosenblatt F. Principles of neurodynamics. Washington, DC: Spartan; 1961.

    Google Scholar 

  11. Witten IH, Frank E. Data mining: practical machine learning tools and techniques. 2nd ed. San Francisco: Morgan Kaufmann; 2003.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Waikato, Hamilton, New Zealand

    Ian H. Witten

Authors
  1. Ian H. Witten
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ian H. Witten .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. School of Elec. Eng. and Computer Science, Seoul National Univ., Seoul, Republic of Korea

    Kyuseok Shim

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Witten, I.H. (2018). Classification. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_552

Download citation

Publish with us

Policies and ethics

Search

Navigation