« Interprétation de la probabilité » : différence entre les versions

Version du 14 décembre 2021 à 12:31

en construction

Définition

Quantifying the probability of an outcome, classification match of an input or confidence in a hypothesis are basic tasks for any machine model to be useful in the real world, let alone to learn and improve itself.

While humans can rely on intuition and their “gut feelings,” artificial intelligence needs a more scientific way to quantify these abstract concepts into functions an algorithm can use and learn from. So for the purposes of machine learning, regardless of the specific method of calculating and interpreting probability that’s employed, all human forms of intuition can be divided into two broad categories : frequentist (physical properties) probability; and Bayesian (Evidentiary) Probability.

Français

interprétation de la probabilité

Anglais

probability interpretation

Source : Wikipeddia (Probability interpretations)

Source : DeepAI.org

@@ Ligne 1 : / Ligne 1 : @@
 ==en construction==
+== Définition ==
+Quantifying the probability of an outcome, classification match of an input or confidence in a hypothesis are basic tasks for any machine model to be useful in the real world, let alone to learn and improve itself.
-== Définition ==
+While humans can rely on intuition and their “gut feelings,” artificial intelligence needs a more scientific way to quantify these abstract concepts into functions an algorithm can use and learn from. So for the purposes of machine learning, regardless of the specific method of calculating and interpreting probability that’s employed, all human forms of intuition can be divided into two broad categories : frequentist (physical properties) probability; and Bayesian (Evidentiary) Probability.
-XXXXXXXXX
 == Français ==
-''' XXXXXXXXX '''
+'''interprétation de la probabilité'''
 == Anglais ==
-''' Probability Interpretation'''
+'''probability interpretation'''
-Quantifying the probability of an outcome, classification match of an input or confidence in a hypothesis are basic tasks for any machine model to be useful in the real world, let alone to learn and improve itself. While humans can rely on intuition and their “gut feelings,” artificial intelligence needs a more scientific way to quantify these abstract concepts into functions an algorithm can use and learn from. So for the purposes of machine learning, regardless of the specific method of calculating and interpreting probability that’s employed, all human forms of intuition can be divided into two broad categories:
+<small>
-<small>
+[https://en.wikipedia.org/wiki/Probability_interpretations  Source : Wikipeddia (Probability interpretations) ]
-[https://deepai.org/machine-learning-glossary-and-terms/probability-interpretations Source : DeepAI.org ]
+[https://deepai.org/machine-learning-glossary-and-terms/probability-interpretations  Source : DeepAI.org ]
 [[Catégorie:DeepAI.org]]
 [[Catégorie:vocabulary]]

« Interprétation de la probabilité » : différence entre les versions