判別モデル(Discriminative Model)と生成モデル(Generative Model)
1988 ワード
第一篇:
Let's say you have input data x and you want to classify the data into labels y. A generative model learns the joint probability distribution p(x,y) and adiscriminative model learns the conditional probability distribution p(y|x) - which you should read as 'the probability of y given x'.
Here's a really simple example. Suppose you have the following data in the form (x,y):
p(x,y) is
p(y|x) is
If you take a few minutes to stare at those two matrices, you will understand the difference between the two probability distributions.
The distribution p(y|x) is the natural distribution for classifying a given example x into a class y, which is why algorithms that model this directly are calleddiscriminative algorithms. Generative algorithms model p(x,y), which can be tranformed into p(y|x) by applying Bayes rule and then used for classification. However, the distribution p(x,y) can also be used for other purposes. For example you could use p(x,y) to generate likely (x,y) pairs.
From the description above you might be thinking that generative models are more generally useful and therefore better, but it's not as simple as that. This paper is a very popular reference on the subject of discriminative vs. generative classifiers, but it's pretty heavy going. The overall gist is that discriminative models generally outperform generative models in classification tasks.
第二篇:
判別モデルDiscriminative Modelは、条件モデル、または条件確率モデルとも呼ばれる.推定は条件確率分布(conditional distribution),p(class|context)である.
生成モデルGenerative Modelは、生成モデルとも呼ばれます.推定は,連合確率分布(joint probability distribution),p(class,context)=p(class|context)*p(context)である.
Let's say you have input data x and you want to classify the data into labels y. A generative model learns the joint probability distribution p(x,y) and adiscriminative model learns the conditional probability distribution p(y|x) - which you should read as 'the probability of y given x'.
Here's a really simple example. Suppose you have the following data in the form (x,y):
(1,0), (1,0), (2,0), (2, 1)
p(x,y) is
y=0 y=1
-----------
x=1 | 1/2 0
x=2 | 1/4 1/4
p(y|x) is
y=0 y=1
-----------
x=1 | 1 0
x=2 | 1/2 1/2
If you take a few minutes to stare at those two matrices, you will understand the difference between the two probability distributions.
The distribution p(y|x) is the natural distribution for classifying a given example x into a class y, which is why algorithms that model this directly are calleddiscriminative algorithms. Generative algorithms model p(x,y), which can be tranformed into p(y|x) by applying Bayes rule and then used for classification. However, the distribution p(x,y) can also be used for other purposes. For example you could use p(x,y) to generate likely (x,y) pairs.
From the description above you might be thinking that generative models are more generally useful and therefore better, but it's not as simple as that. This paper is a very popular reference on the subject of discriminative vs. generative classifiers, but it's pretty heavy going. The overall gist is that discriminative models generally outperform generative models in classification tasks.
第二篇:
判別モデルDiscriminative Modelは、条件モデル、または条件確率モデルとも呼ばれる.推定は条件確率分布(conditional distribution),p(class|context)である.
生成モデルGenerative Modelは、生成モデルとも呼ばれます.推定は,連合確率分布(joint probability distribution),p(class,context)=p(class|context)*p(context)である.