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总是听到这两个术语,但是又一直不清楚它们最本质的区别。今天花了一小点时间来彻底的弄清楚了。得到的结论如下:
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)

转自http://billlangjun.blogspot.com/2006/09/discriminative-vs-generative-model.html

posted on 2008-09-23 10:33 Bati 阅读(...) 评论(...) 编辑 收藏