Model-based Learning Algorithm
From GM-RKB
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).
- AKA: Supervised Model-based Learning Algorithm, Model-based Training Algorithm.
- Context:
- 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 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.
- Example(s):
- a Regression Algorithm, such as a least-squares function fitting algorithm.
- a Statistical Modeling Algorithm, such as s Linear Model Learning Algorithm or a Logistic Regression Model Learning Algorithm
- a Decision Tree Learning Algorithm, such as a C4.5 Algorithm.
- a Discriminative Machine Learning Algorithm, such as a logistic regression.
- a Neural-Network Learning Algorithm, such as a backprop learning algorithm.
- a Kernel-based Learning Algorithm, such as an SVM algorithm.
- Counter-Example(s):
- See: Unsupervised Learning Algorithm
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.