Probabilistic Learning Algorithm

A Probabilistic Learning Algorithm is a supervised model-based learning algorithm that learns a probabilistic model.

• Context:
  • It can range from being a Discriminative Learning Algorithm to being a Generative Learning Algorithm.
  • …
• Example(s):
  • Naive-Bayes Learning Algorithm.
  • Hidden Markov Model Learning Algorithm.
  • …
• Counter-Example(s):
  • NNet Learning.
  • SVM Learning.
  • Decision Tree Learning.
• See: Uncertain Reasoning, Learning Algorithm.

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References

1999

• (Jaakkola & Haussler, 1999) ⇒ Tommi S. Jaakkola, and David Haussler. (1999). “Exploiting Generative Models in Discriminative Classifiers.” In: Proceedings of the 1998 conference on Advances in Neural Information Processing Systems II. ISBN:0-262-11245-0
  • QUOTE: Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often result in classification performance superior to that of the model based approaches.

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