产生式模型与判别式模型

一、产生式模型（Generative model）概念（来自wiki）

       In probability and statistics, a generative model is a model for randomly generating observable data, typically given some hidden parameters. It specifies a joint probability distribution over observation and label sequences. Generative models are used in machine learning for either modeling data directly (i.e., modeling observations drawn from aprobability density function), or as an intermediate step to forming a conditional probability density function. A conditional distribution can be formed from a generative model through Bayes' rule.

二、常见产生式模型

1. Gaussian mixture model and other types of mixture model
2. Hidden Markov model
3. Probabilistic context-free grammar
4. Naive Bayes
5. Averaged one-dependence estimators
6. Latent Dirichlet allocation
7. Restricted Boltzmann machine

三、判别式模型（Discriminative model）概念（来自wiki)

Discriminative models, also called conditional models, are a class of models used in machine learning for modeling the dependence of an unobserved variable y on an observed variable x. Within a probabilistic framework, this is done by modeling the conditional probability distribution P(y|x), which can be used for predicting y from x.

四、常见的判别式模型

1.  Logistic regression, a type of generalized linear regression used for predicting binary or categorical outputs (also known as maximum entropy classifiers)
2. Linear discriminant analysis
3. Support vector machines
4. Boosting (meta-algorithm)
5. Conditional random fields
6. Linear regression
7. Neural networks