Model-based Learning Algorithm

A Model-based Learning Algorithm is a supervised learning algorithm that produces a model-based prediction function (in a higher-level formalism than the training instances).

• AKA: Supervised Model-based Learning Algorithm, Model-based Machine Learning Algorithm, Model-based Training Algorithm.
• Context:
  • It can solve a model-based learning task (that requires the production of a model-based prediction function).
  • It can range from being a Supervised Model-based Classification Algorithm to being a Supervised Model-based Estimation Algorithm
  • It can range from being a Discriminative Learning Algorithm to being a Generative Learning Algorithm.
  • It can range from being a Lazy Model-based Learning Algorithm to being an Eager Model-based Learning Algorithm.
• Example(s):
  • a Regression Algorithm.
  • a Statistical Modeling Algorithm.
    • Linear Model Learning Algorithm.
    • Logistic Regression Model Learning Algorithm
  • C4.5, a Decision Tree Learning Algorithm.
  • a Naive Bayes Algorithm (Generative Machine Learning Algorithm)
  • a Logistic Regression Algorithm (Discriminative Machine Learning Algorithm)
  • a Linear Regression Algorithm.
• Counter-Example(s):
  • an Instance-based Learning Algorithm, such as: k-Nearest Neighbor Algorithm.
  • a Locally Weighted Regression Algorithm.
• See: Supervised Learning Algorithm

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References

1993

• (Quinlan, 1993) ⇒ J. Ross Quinlan. (1993). "Combining Instance-based and Model-based Learning." In: Proceedings of the Tenth International Conference on Machine Learning.

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