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):
  • a Naive-Bayes Classification Algorithm.
  • ...
• Counter-Example(s):.
  • a Discriminative Learning Algorithm.
• 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]\displaystyle{ x }[/math] and their labels [math]\displaystyle{ y }[/math] arise from an unknown joint probability [math]\displaystyle{ p(x,y) }[/math]. If we can approximate [math]\displaystyle{ p(x,y) }[/math] using a parametric family of models [math]\displaystyle{ 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.

• Concept