Model-based Learning Algorithm: Difference between revisions

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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 instance]]s).
A [[Model-based Learning Algorithm]] is a [[learning algorithm]] that can be applied by a [[model-based learning system]] to solve a [[model-based learning task]] (which requires the production of a [[model-based prediction function]]).
* <B><U>AKA</U>:</B> [[Supervised Model-based Learning Algorithm]], [[Model-based Machine Learning Algorithm]], [[Model-based Training Algorithm]].
* <B>AKA:</B> [[Supervised Model-based Learning Algorithm]], [[Model-based Learning Algorithm|Model-based Training Algorithm]].
* <B><U>Context</U>:</B>
* <B>Context</U>:</B>
** 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 Learning Algorithm]] to being an [[Unsupervised Model-based Learning Algorithm]].
** 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 [[Model-based Classification Algorithm]] to being a [[Model-based Ranking Algorithm]] to being a [[Model-based Estimation Algorithm]].
** It can range from being a [[Lazy Model-based Learning Algorithm]] to being an [[Eager Model-based Learning Algorithm]].
** It can range from being a [[Discriminative Learning Algorithm]] to being a [[Generative Learning 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]].
* <B>Example(s):</B>  
* <B><U>Example(s)</U>:</B>  
** a [[Regression Algorithm]], such as a [[least-squares function fitting algorithm]].
** a [[Regression Algorithm]].
** a [[Statistical Modeling Algorithm]], such as s [[Linear Model Learning Algorithm]] or a [[Logistic Regression Model Learning Algorithm]].
** a [[Statistical Modeling Algorithm]].
** a [[Decision Tree Learning Algorithm]], such as a [[C4.5 Algorithm]].
*** [[Linear Model Learning Algorithm]].
** a [[Discriminative Machine Learning Algorithm]], such as a [[logistic regression]].
*** [[Logistic Regression Model Learning Algorithm]]
** a [[Neural-Network Learning Algorithm]], such as a [[backprop learning algorithm]].
** [[C4.5]], a [[Decision Tree Learning Algorithm]].
** a [[Kernel-based Learning Algorithm]], such as an [[SVM algorithm]].
** a [[Naive Bayes Algorithm]] ([[Generative Machine Learning Algorithm]])
** …
** a [[Logistic Regression Algorithm]] ([[Discriminative Machine Learning Algorithm]])
* <B>Counter-Example(s):</B>  
** a [[Linear Regression Algorithm]].
* <B><U>Counter-Example(s)</U>:</B>  
** an [[Instance-based Learning Algorithm]], such as: [[k-Nearest Neighbor Algorithm]].
** an [[Instance-based Learning Algorithm]], such as: [[k-Nearest Neighbor Algorithm]].
** a [[Locally Weighted Regression Algorithm]].
** a [[Locally Weighted Regression Algorithm]].
* <B><U>See</U>:</B> [[Supervised Learning Algorithm]]
* <B>See</U>:</B> [[Unsupervised Learning Algorithm]].
 
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==References ==


===1993===
== References ==
* (Quinlan, 1993) &rArr; [[J. Ross Quinlan]]. (1993). "Combining Instance-based and Model-based Learning." In: Proceedings of the Tenth International Conference on Machine Learning.
 
=== 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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Latest revision as of 05:19, 28 November 2023

A Model-based Learning Algorithm is a learning algorithm that can be applied by a model-based learning system to solve a model-based learning task (which requires the production of a model-based prediction function).



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.