« Classification naïve bayésienne multinomiale » : différence entre les versions

Dernière version du 29 janvier 2024 à 10:08

Définition

Classification naïve bayésienne où les attributs suivent une distribution multinomiale.

Compléments

On emploie parfois polynomiale à la place de multinomiale et bayésienne naïve plutôt que naïve bayésienne.

Français

classification naïve bayésienne multinomiale

classification naïve de Bayes multinomiale

classification naïve bayésienne polynomiale

classification naïve de Bayse polynomiale

Anglais

multinomial naïve Bayes classification

Sources

Source : Wikipedia Machine Learning

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-==en construction==
+== Définition ==
+[[Classification naïve bayésienne]] où les attributs suivent une [[distribution multinomiale]].
-== Définition ==
+==Compléments==
-XXXXXXXXX
+On emploie parfois ''polynomiale'' à la place de ''multinomiale'' et ''bayésienne naïve'' plutôt que ''naïve bayésienne''.
 == Français ==
-''' XXXXXXXXX '''
+''' classification naïve bayésienne multinomiale '''
+''' classification naïve de Bayes multinomiale '''
+''' classification naïve bayésienne polynomiale '''
+''' classification naïve de Bayse polynomiale '''
 == Anglais ==
-''' Multinomial naïve Bayes'''
+''' multinomial naïve Bayes classification'''
+<!-- With a multinomial event model, samples (feature vectors) represent the frequencies with which certain events have been generated by a multinomial {\displaystyle (p_{1},\dots ,p_{n})}(p_1, \dots, p_n) where {\displaystyle p_{i}}p_{i} is the probability that event i occurs (or K such multinomials in the multiclass case). A feature vector {\displaystyle \mathbf {x} =(x_{1},\dots ,x_{n})}{\mathbf  {x}}=(x_{1},\dots ,x_{n}) is then a histogram, with {\displaystyle x_{i}}x_{i} counting the number of times event i was observed in a particular instance. This is the event model typically used for document classification, with events representing the occurrence of a word in a single document (see bag of words assumption). The likelihood of observing a histogram x is given by
+-->
-With a multinomial event model, samples (feature vectors) represent the frequencies with which certain events have been generated by a multinomial {\displaystyle (p_{1},\dots ,p_{n})}(p_1, \dots, p_n) where {\displaystyle p_{i}}p_{i} is the probability that event i occurs (or K such multinomials in the multiclass case). A feature vector {\displaystyle \mathbf {x} =(x_{1},\dots ,x_{n})}{\mathbf  {x}}=(x_{1},\dots ,x_{n}) is then a histogram, with {\displaystyle x_{i}}x_{i} counting the number of times event i was observed in a particular instance. This is the event model typically used for document classification, with events representing the occurrence of a word in a single document (see bag of words assumption). The likelihood of observing a histogram x is given by
+==Sources==
-<small>
 [https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_na%C3%AFve_Bayes  Source : Wikipedia  Machine Learning ]
+[[Catégorie:GRAND LEXIQUE FRANÇAIS]]
-[[Catégorie:vocabulary]]
-[[Catégorie:Wikipedia-IA‎]]