Machine Learning Series No.6 -- EM algorithm
EM算法 1.直观理解 通俗理解：http://www.javashuo.com/article/p-asqtxiud-mh.html 通俗的理解看出就是EM算法由于不知道隐变量的分布，先给出参数的随机初始值，然后根据参数，去得到隐变量的分布，然后根据隐变量和观测变量的共同分布基于最大似然去重新估计参数，知道参数稳定。 2.数学推导 极大似然估计： L(θ)=∑ilogp(xi;θ)=∑ilog(
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相关文章
- 1. Machine Learning Series No.4 -- Decision Trees
- 2. Machine Learning Series No.10 -- PCA
- 3. Machine Learning Series No.7 -- Max Entropy
- 4. Machine Learning Series No.2 --Logistic Regression
- 5. Machine Learning Series No.3-- Support Vector Machine
- 6. machine learning[GMM-EM]
- 7. Machine Learning Series No.8 -- CRF(Conditional Random field)
- 8. Machine Learning in Action:KNN Algorithm
- 9. Machine Learning(8): Reinforcement learning algorithm
- 10. Machine learning(6): Quiz 6