Generative Model and Discrimitive Model

http://en.wikipedia.org/wiki/Generative_model

http://workingmidi.blogspot.com/2007/06/generative-model.html

http://blog.csdn.net/hitmonkey/archive/2008/05/15/2448906.aspx

以前对这两个概念非常模糊，今天从网上找到了关于这两个概念的两种风格的解释，比较一下！
第一种风格：

For a data sample: x and it class lable:,y,
要检测图像中的淋巴结，有很多positive，很多的negetive。
Discrimitive model: p(y|x), 给定x,算y。

Generative model:和 discrimitive model 的区别：
Generative model focus
在自己的inclass 本身，不care 到底 decision boundary 在哪。
Generative model 实际上带的information 要比discrimitive model rich,
因为假设有generative model， 两类的，就完全得到了p(x|y)，而discrimitive model 只care
decision boundary。这里说的generative model 和 discrimitive
model，在行业里，这个说法是通用的。
由Generative model 可以得到 discrimitive model, 但由discrimitive model 得不到
generative model。因为需要用到P(x), 如果只是label 的话，p(y)很简单，但为什么不能直接用Generative
model 呢？

优缺

Discrimitive model: 相当于在图像上scan 一下，detection, 用一个path, 在不同的scale
上search, 每一词看probability, 在SVM上是positive 还是negetive。
Discrimitive 比较easy to learn， 给出正负例，给出lable, focus on discrimitive
model marginal distribution。 某种意义上，比generativemodel 要简单，但power 是
limited, 可以告诉你的时1还是2，但没有办法把整个场景描述出来。
P(x|y) P(y)
当一个分类，没有negetive，研究single class，比discrimitive model flex 多，learning 和
computing 要比 discrimitive model 复杂得多

第二种解释

Discriminative Model是判别模型，又可以称为条件模型，或条件概率模型。
Generative Model是生成模型，又叫产生式模型。

二者的本质区别是
discriminative model 估计的是条件概率分布(conditional
distribution)p(class|context)
generative model 估计的是联合概率分布（joint probability distribution）p()
常见的Generative Model主要有：
– Gaussians, Naive Bayes, Mixtures of multinomials
– Mixtures of Gaussians, Mixtures of experts, HMMs
– Sigmoidal belief networks, Bayesian networks
– Markov random fields

常见的Discriminative Model主要有：
– logistic regression
– SVMs
– traditional neural networks
– Nearest neighbor

Successes of Generative Methods：

NLP
– Traditional rule-based or Boolean logic systems
Dialog and Lexis-Nexis) are giving way to statistical
approaches (Markov models and stochastic context
grammars)
Medical Diagnosis
– QMR knowledge base, initially a heuristic expert
systems for reasoning about diseases and symptoms
been augmented with decision theoretic formulation
Genomics and Bioinformatics
– Sequences represented as generative HMMs

主要应用Discriminative Model：

Image and document classification
Biosequence analysis
Time series prediction

Discriminative Model缺点：
Lack elegance of generative
– Priors, structure, uncertainty
Alternative notions of penalty functions,
regularization, kernel functions

Feel like black-boxes
– Relationships between variables are not explicit
and visualizable

Bridging Generative and Discriminative：
Can performance of SVMs be combined
elegantly with flexible Bayesian statistics?

Maximum Entropy Discrimination marries
both methods
– Solve over a distribution of parameters (a
distribution over solutions)