« Surface d'erreur » : différence entre les versions


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== en construction ==
[[Catégorie:Vocabulary]]
[[Catégorie:Apprentissage automatique‏‎]]
[[Catégorie:UNSW]]
== Définition ==
== Définition ==
Lorsque l'erreur totale d'un réseau neuronal formé par rétropropagation est exprimée en fonction des poids et représentée graphiquement (dans la mesure où cela est possible avec un grand nombre de poids), le résultat est une surface appelée surface d'erreur.
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== Français ==
== Français ==
'''surface d'erreur'''
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== Anglais ==
== Anglais ==
'''error surface'''
'''error surface'''
When total error of a backpropagation-trained neural network is expressed as a function of the weights, and graphed (to the extent that this is possible with a large number of weights), the result is a surface termed the error surface. The course of learning can be traced on the error surface: as learning is supposed to reduce error, when the learning algorithm causes the weights to change, the current point on the error surface should descend into a valley of the error surface.
The "point" defined by the current set of weights is termed a point in weight space. Thus weight space is the set of all possible values of the weights.
See also local minimum and gradient descent.


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[http://www.cse.unsw.edu.au/~billw/dictionaries/mldict.html      Source : INWS machine learning dictionary]  ]
[http://www.cse.unsw.edu.au/~billw/dictionaries/mldict.html      Source : INWS machine learning dictionary]  ]
[[Catégorie:Vocabulary]]
[[Catégorie:Apprentissage automatique‏‎]]
[[Catégorie:UNSW]]

Version du 24 octobre 2021 à 12:32

en construction


Définition

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Français

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Anglais

error surface

When total error of a backpropagation-trained neural network is expressed as a function of the weights, and graphed (to the extent that this is possible with a large number of weights), the result is a surface termed the error surface. The course of learning can be traced on the error surface: as learning is supposed to reduce error, when the learning algorithm causes the weights to change, the current point on the error surface should descend into a valley of the error surface.

The "point" defined by the current set of weights is termed a point in weight space. Thus weight space is the set of all possible values of the weights.

See also local minimum and gradient descent.

Source : INWS machine learning dictionary ]



Contributeurs: Claire Gorjux, wiki