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根本区别在于是否计算了联合分布 P ( X , Y ) P(X,Y) P(X,Y)和是否比较了模型输出的概率大小.
<>生成式模型的特点
* 对联合分布进行建模,然后通过朴素贝叶斯来求条件概率,选择使得条件概率最大的 Y Y Y
* 可以还原出联合概率分布 P ( X , Y ) P(X,Y) P(X,Y)
* 学习收敛速度快,当样本容量增加时,学到的模型可以更快地收敛于真实模型
* 应对存在隐变量(不可观测的变量)的场景
* 相比于判别式模型,往往模型效果差一些
* 学习到的数据本身信息更多,能反应数据本身特性
* 学习成本较高,需要更多的计算资源
* 需要的样本数更多,样本较少时学习效果较差
<>判别式模型的特点
* 不能还原出联合概率分布 P ( X , Y ) P(X,Y) P(X,Y)
* 不能处理存在隐变量的场景
* 由于直接学习的是条件概率 P ( X ∣ Y ) P(X|Y) P(X∣Y)或者决策函数 f ( X ) f(X) f(X),往往学习的准确率更高
* 由于直接学习的是 P ( X ∣ Y ) P(X|Y) P(X∣Y)或者 f ( X ) f(X) f(X)
,可以对数据进行各种程序熵的抽象、定义特征并使用特征,可以简化学习问题
* 对条件概率建模,学习不同类别之间的最优边界
* 捕捉不同类别特征的差异信息,不学习本身分布信息,无法反映数据本身特性
* 学习成本较低,需要的计算资源较少
* 需要的样本数可以较少,少样本也能很好学习
<>典型的模型
* 常见的判别式模型有:线性回归、限行判别分析、SVM、神经网络、K近邻、决策树、最大熵模型、boosting、条件随机场
* 常见的生成式模型: HMM、朴素贝叶斯、GMM、LDA等