判别模型(descriminative model)+生成模型(generative model)
来源:互联网 发布:淘宝被差评有什么影响 编辑:程序博客网 时间:2024/04/30 10:17
判别模型(descriminative model)+生成模型(generative model)
原文地址:http://blog.sina.com.cn/s/blog_4c740ac00100vfew.html
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 ondiscrimitive
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 probabilitydistribution)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)
- 判别模型(descriminative model)+生成模型(generative model)
- 什么是判别模型(Discriminative Model)和生成模型(Generative Model)
- 判别模型(discriminative model) 和 生成模型(generative model)
- 判别模型(Discriminative model)和生成模型(Generative model)
- 什么是判别模型(Discriminative Model)和生成模型(Generative Model)
- 判别模型(Discriminative Model)和生成模型(Generative Model)
- 关于生成模型和判别模型 --- generative model vs discriminative model
- 生成模型(Generative Model)Vs 判别模型(Discriminative Model)
- 生成模型(Generative Model)Vs 判别模型(Discriminative Model)
- 生成模型(generative model)和判别模型(discriminative model)
- 17.5.8 生成模型(Generative model)和判别模型(Discriminative model)的区别
- 生成模型(generative model)与判别模型(discriminate)的联系及区别
- 判决模型(discriminative model)和生成模型(generative model)
- 判别式模型和生成式模型的区别(discriminative model and generative model)
- 判别式模型(discriminative model) 和生成式模型(generative model)的区别
- 生成模型(Generative)和判别模型(Discriminative)
- 机器学习入门系列05,Classification: Probabilistic Generative Model(分类:概率生成模型)
- 判别式模型和产生式模型 (discriminative model and generative model)
- shell脚本系列-正则表达式介绍
- 牛腩新闻发布系统之发布
- 递归法
- 谈谈程序在内存中的分布
- 多项式参数估计——最小二乘法
- 判别模型(descriminative model)+生成模型(generative model)
- C语言连接数据库
- 1010:平均绩点
- leetcode-Binary Tree Zigzag Level Order Traversal
- Genesis-3D 入门教程—18.基本UI及运行
- Java备忘录模式(Memento)
- HDU 3531 Match(树状数组)
- 快速傅里叶变换(蝶形算法)
- JavaWeb开发中遇到的错误:org.apache.catalina.core.StandardWrapperValve invoke