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) (Bishop, 2007 Jaakkola & Haussler, 1999; Jaakkola, Meila & Jebara, 1999; Lasserre, Bishop & Minka, 2006; Ng & Jordan, 2002), where y is a class and x is a description of an object to be classified. Generative learning contrasts with 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 maps from input x onto the output y (such as support vector machines, neural networks, and decision trees), where the decision risk is minimized without estimation of P(y, x), P(x | y) or P(y | x) (Ja...
This is a preview of subscription content, log in via an institution to check access.
References
Bishop, C. M. (2007). Pattern recognition and machine learning. Springer.
Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. Proceedings of the eleventh annual conference on Computational learning theory, Madison, Wisconsin, USA. New York:Â ACM.
Chapelle, O., Schölkopf, B., & Zien, A. (2006). Semi-supervised learning. Cambridge: The MIT Press.
Efron, B. (1975). The efficiency of logistic regression compared to normal discriminant analysis. Journal of the American Statistical Association, 70(352), 892–898.
Jaakkola, T. S., & Haussler, D. (1999). Exploiting generative models in discriminative classifiers. Advances in neural information processing systems, 11.
Jaakkola, T., Meila, M., & Jebara, T. (1999). Maximum entropy discrimination. Advances in neural information processing systems, 12.
Lasserre, J. A., Bishop, C. M., & Minka, T. P. (2006). Principled hybrids of generative and discriminative models. IEEE Conference on Computer Vision and Pattern Recognition, New York.
Ng, A. Y., & Jordan, M. I. (2002). On discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes. Advances in neural information processing systems, 14.
Taskar, B., Guestrin, C., & Koller, D. (2004). Max-margin Markov networks. Advances in neural information processing systems, 16.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this entry
Cite this entry
Liu, B., Webb, G.I. (2011). Generative and Discriminative Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_332
Download citation
DOI: https://doi.org/10.1007/978-0-387-30164-8_332
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30768-8
Online ISBN: 978-0-387-30164-8
eBook Packages: Computer ScienceReference Module Computer Science and Engineering