Skip to main content
SpringerLink
Log in

Learning Curves in Machine Learning

  • Reference work entry
  • First Online:
Encyclopedia of Machine Learning and Data Mining

Synonyms

Error curve; Experience curve; Improvement curve; Training curve

Definition

A learning curve shows a measure of predictive performance on a given domain as a function of some measure of varying amounts of learning effort. The most common form of learning curves in the general field of machine learning shows predictive accuracy on the test examples as a function of the number of training examples as in Fig. 1.

Learning Curves in Machine Learning, Fig. 1
figure 126 figure 126

Stylized learning curve showing the model accuracy on test examples as a function of the number of training examples

Full size image

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 699.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 949.99
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

  • Cortes C, Jackel LD, Solla SA, Vapnik V, Denker JS (1994) Learning curves: asymptotic values and rate of convergence. Adv Neural Inf Process Syst 6:327–334

    Google Scholar 

  • Flury BW, Schmid MJ (1994) Error rates in quadratic discrimination with constraints on the covariance matrices. J Classif 11:101–120

    Article  MathSciNet  MATH  Google Scholar 

  • Kibler D, Langley P (1988) Machine learning as an experimental science. In: Proceedings of the third European working session on learning, Pittman, Glasgow. Kluwer Academic, Hingham, pp 81–92

    Google Scholar 

  • Perlich C, Provost F, Simonoff J (2003) Tree induction vs. logistic regression: a learning-curve analysis. J Mach Learn Res 4:211–255

    MathSciNet  MATH  Google Scholar 

  • Shavlik JW, Mooney RJ, Towell GG (1991) Symbolic and neural learning algorithms: an experimental comparison. Mach Learn 6:11–143

    MATH  Google Scholar 

  • Wozniak RH (1999) Introduction to memory: Hermann Ebbinghaus (1885/1913). In: Classics in the history of psychology. Thoemmes Press, Bristol

    Google Scholar 

  • Wright TP (1936) Factors affecting the cost of airplanes. J Aeronaut Sci 3(4):122–128

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IBM T.J. Watson Research Center, Yorktown Heights, NY, USA

    Claudia Perlich

Authors
  1. Claudia Perlich
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this entry

Cite this entry

Perlich, C. (2017). Learning Curves in Machine Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_452

Download citation

Publish with us

Policies and ethics

Search

Navigation