Discriminative models are a class of models used in machine learning for modeling the dependence of an unobserved variable y on an observed variable x. Within a statistical framework, this is done by modeling the conditional probability distribution P(y | x), which can be used for predicting y from x.
Discriminative models differ from generative models in that they do not allow one to generate samples from the joint distribution of x and y.
Examples of discriminative models used in machine learning include:
- Linear discriminant analysis
- Support vector machines
- Boosting
- Conditional random fields
- Logistic regression
- Neural Networks
See also
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