Skip to main content

Generative Learning

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

Definition

Generative learning refers alternatively to any classification learning process that classifies by using an estimate of the joint probability P(y, x) or to any classification learning process that classifies by using estimates of the prior probability P(y) and the conditional probability P(x | y), where y is a class and x is a description of an object to be classified. Given such models or estimates it is possible to generate synthetic objects from the joint distribution. Generative learning contrasts to discriminative learning in which a model or estimate of P(y | x) is formed without reference to an explicit estimate of any of P(x), P(y, x), or P(x | y).

Cross-References

Generative and Discriminative Learning

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

Access this chapter

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

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this entry

Cite this entry

(2017). Generative 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_333

Download citation

Publish with us

Policies and ethics