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Generative Model and Discrimitive Model

Not very easy to distinguish between them, here I just give some useful link to this two kinds of model.
For a data sample: x and it class lable:,y,
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
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?
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
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。
discriminative model 估计的是条件概率分布(conditional
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:
– Traditional rule-based or Boolean logic systems
Dialog and Lexis-Nexis) are giving way to statistical
approaches (Markov models and stochastic context
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)


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