Latent Variable Model Training Algorithm: Difference between revisions
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A [[Latent Variable Model Training Algorithm]] is a [[model training algorithm]] that ... | A [[Latent Variable Model Training Algorithm]] is a [[model training algorithm]] that can be applied by a [[latent variable model training system]] (to solve a [[latent variable model training task]]. | ||
* <B>Context:</B> | |||
** It can range from being a [[Parametric Latent Variable Modeling Algorithm]] to being a [[Non-Parametric Latent Variable Modeling Algorithm]]. | |||
** It can range from being a [[Latent Semantic Modeling Algorithm]] to being a [[Latent Syntactic Modeling Algorithm]]. | |||
* <B>Example(s):</B> | * <B>Example(s):</B> | ||
** [[E-M Algorithm]]. | ** [[E-M Algorithm]]. | ||
** [[Latent Semantic Indexing Algorithm]]. | |||
* <B>See:</B> [[Latent Variable]]. | * <B>See:</B> [[Latent Variable]]. | ||
---- | ---- | ||
---- | ---- | ||
== References == | |||
=== 2013 === | |||
* (Wikipedia, 2013) ⇒ http://en.wikipedia.org/wiki/Expectation-maximization_algorithm | |||
** … an [[iterative method]] for finding [[maximum likelihood]] or [[maximum a posteriori]] (MAP) estimates of [[parameter]]s in [[statistical model]]s, where the [[latent variable model|model]] depends on [[unobserved latent variable]]s. | |||
---- | |||
__NOTOC__ | |||
[[Category:Concept]] | |||
Latest revision as of 12:24, 2 August 2022
A Latent Variable Model Training Algorithm is a model training algorithm that can be applied by a latent variable model training system (to solve a latent variable model training task.
- Context:
- It can range from being a Parametric Latent Variable Modeling Algorithm to being a Non-Parametric Latent Variable Modeling Algorithm.
- It can range from being a Latent Semantic Modeling Algorithm to being a Latent Syntactic Modeling Algorithm.
- Example(s):
- See: Latent Variable.
References
2013
- (Wikipedia, 2013) ⇒ http://en.wikipedia.org/wiki/Expectation-maximization_algorithm
- … an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables.