生成式模型 VS 判别式模型
Discriminative models learn the classification (hard or soft) boundary between classes. A discriminative model learns the conditional probability distribution p(y|x) - which you should read as “the probability of y given x”.
Generative models model the distribution of individual classes. A generative model learns the joint probability distribution p(x,y)
生成模型是模拟这个结果是如何产生的,然后算出产生各个结果的概率。判别模型是发现各个结果之间的不同,不关心产生结果的过程。
典型代表模型
生成式模型
- 朴素贝叶斯
- K紧邻(KNN)
- 混合高斯模型
- 隐马尔科夫模型(HMM)
- 贝叶斯网络
- Sigmoid Belief Networks
- 马尔科夫随机场(Markov Random Fields)
- 深度信念网络(DBN)
判别式模型
- 线性回归(Linear Regression)
- 逻辑斯蒂回归(Logistic Regression)
- 神经网络(NN)
- 支持向量机(SVM)
- 高斯过程(Gaussian Process)
- 条件随机场(CRF)
- CART(Classification and Regression Tree)
参考
[1] On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes
[2] The difference between a generative and a discriminative algorithm?
[3] 判别式模型与生成式模型