« Théorème No Free Lunch » : différence entre les versions
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== en construction == | == en construction == | ||
== Définition == | |||
== | == Français == | ||
'''théorème No Free Lunch''' | |||
'''théorème NFL''' | |||
'''théorème « Rien n'est gratuit! »''' | |||
== Anglais == | |||
'''No Free Lunch theorem''' | |||
'''NFL theorem''' | |||
''' | |||
The No Free Lunch Theorem, often abbreviated as NFL or NFLT, is a theoretical finding that suggests all optimization algorithms perform equally well when their performance is averaged over all possible objective functions. | |||
There are, generally speaking, two No Free Lunch (NFL) theorems: one for machine learning and one for search and optimization. These two theorems are related and tend to be bundled into one general axiom (the folklore theorem). | |||
TANSTAFL - there ain't no such thing as a free lunch. | |||
[https://machinelearningmastery.com/no-free-lunch-theorem-for-machine-learning/ Source : machinelearningmastery.com ] | |||
[https://www.kdnuggets.com/2019/09/no-free-lunch-data-science.html Source : KDnuggets ] | |||
[https://www.btb.termiumplus.gc.ca/tpv2alpha/alpha-fra.html?lang=fra&i=1&srchtxt=NO+FREE+LUNCH+THEOREM&codom2nd_wet=1#resultrecs Source : TERMIUM Plus ] | |||
[[Catégorie:Vocabulaire]] | [[Catégorie:Vocabulaire]] |
Version du 7 septembre 2021 à 10:07
en construction
Définition
Français
théorème No Free Lunch
théorème NFL
théorème « Rien n'est gratuit! »
Anglais
No Free Lunch theorem
NFL theorem
The No Free Lunch Theorem, often abbreviated as NFL or NFLT, is a theoretical finding that suggests all optimization algorithms perform equally well when their performance is averaged over all possible objective functions.
There are, generally speaking, two No Free Lunch (NFL) theorems: one for machine learning and one for search and optimization. These two theorems are related and tend to be bundled into one general axiom (the folklore theorem).
TANSTAFL - there ain't no such thing as a free lunch.
Contributeurs: Jean Benoît Morel, wiki