Supervised Model-based Estimation Algorithm

A Supervised Model-based Estimation Algorithm is a supervised estimation algorithm that is a model-based learning algorithm.

• AKA: Regression Method.
• Context:
  • It can range from being an Eager Model-based Estimation Algorithm to being a Lazy Model-based Estimation Algorithm.
  • It can be solved by a Model-based Estimation System (to solve a model-based estimation task).
• Example(s):
  • a Least-Squares Regression Algorithm.
  • a Generalized Linear Regression Algorithm.
  • …
• Counter-Example(s):
  • Instance-based Estimation Algorithm.
  • Model-based Classification Algorithm.
• See: Model-based.

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References

2006

• (Tibshirani, 1996) ⇒ Robert Tibshirani. (1996). “Regression Shrinkage and Selection via the Lasso.” In: Journal of the Royal Statistical Society, Series B, 58(1).
  • QUOTE: Consider the usual regression situation: we have data [math]\displaystyle{ (\mathbf{x}^i, y^i), i=1,2,...,N \ , }[/math] where [math]\displaystyle{ \mathbf{x}^i=(x_{i1},..., x_{ip})^T }[/math] and [math]\displaystyle{ y_i }[/math] are the regressors and response for the ith observation. The ordinary least squares (OLS) estimates are obtained by minimizing the residual squared error.

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