Skip to main content

Generative and Discriminative Learning

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

Synonyms

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) (Jaakkola and Haussler 1999; Jaakkola et al. 1999; Ng and Jordan 2002; Lasserre et al. 2006; Bishop 2007), 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(y, x), P(x), or P(x | y).

It is also common to categorize as discriminative approaches based on a decision function that directly map from input x onto the output y (such as support vector machines, neural networks, and decision trees)...

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

Access this chapter

Institutional subscriptions

Recommended Reading

  • Bishop CM (2007) Pattern recognition and machine learning. Springer, New York

    MATH  Google Scholar 

  • Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on computational learning theory

    Book  Google Scholar 

  • Chapelle O, Schölkopf B, Zien A (2006) Semi-supervised learning. The MIT Press, Cambridge

    Book  Google Scholar 

  • Efron B (1975) The efficiency of logistic regression compared to normal discriminant analysis. J Am Stat Assoc 70(352):892–898

    Article  MathSciNet  MATH  Google Scholar 

  • Jaakkola TS, Haussler D (1999) Exploiting generative models in discriminative classifiers. Adv Neural Inf Process Syst 11:487–493

    Google Scholar 

  • Jaakkola T, Meila M, Jebara T (1999) Maximum entropy discrimination. Adv Neural Inf Process Syst 12

    Google Scholar 

  • Lasserre JA, Bishop CM, Minka TP (2006) Principled hybrids of generative and discriminative models. In: IEEE conference on computer vision and pattern recognition

    Book  Google Scholar 

  • Ng AY, Jordan MI (2002) On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. Adv Neural Inf Process Syst 2(14):841–848

    Google Scholar 

  • Taskar B, Guestrin C, Koller D (2004) Max-margin Markov networks. Adv Neural Inf Process Syst 16

    Google Scholar 

  • Zaidi N, Carman M, Webb GI (2014) Naive-Bayes inspired effective pre-conditioner for speeding-up logistic regression. In: Proceedings of the 14th IEEE international conference on data mining, ICDM-14, pp 1097–1102

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bin Liu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media New York

About this entry

Cite this entry

Liu, B., Webb, G.I. (2016). Generative and Discriminative Learning. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_113-1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-7502-7_113-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Boston, MA

  • Online ISBN: 978-1-4899-7502-7

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics