# Generative Classification Algorithm

A Generative Classification Algorithm is a generative learning algorithm that can solve a supervised classification task.

**Context:**- It can range from being a Fully-Supervised Generative Classification Algorithm to being a Semi-Supervised Generative Classification Algorithm.
- It can estimate a Class-Conditional Density.
- It can use a Parametric Model

**Example(s):****Counter-Example(s):**.**See:**Generative Classification Function, Generative Adversarial Network.

## References

## 2004

- (Bouchard & Triggs, 2004) ⇒ Guillaume Bouchard, and Bill Triggs. (2004). “The Trade-off Between Generative and Discriminative Classifiers.” In: Proceedings of COMPSTAT 2004.
- QUOTE: In supervised classification, inputs [math]x[/math] and their labels [math]y[/math] arise from an unknown joint probability [math]p(x,y)[/math]. If we can approximate [math]p(x,y)[/math] using a parametric family of models [math]G = \{p_θ(x,y),\theta \in \Theta\}[/math], then a natural classifier is obtained by first estimating the class-conditional densities, then classifying each new data point to the class with highest posterior probability. This approach is called
*generative*classification.

- QUOTE: In supervised classification, inputs [math]x[/math] and their labels [math]y[/math] arise from an unknown joint probability [math]p(x,y)[/math]. If we can approximate [math]p(x,y)[/math] using a parametric family of models [math]G = \{p_θ(x,y),\theta \in \Theta\}[/math], then a natural classifier is obtained by first estimating the class-conditional densities, then classifying each new data point to the class with highest posterior probability. This approach is called