标签:use models ati com combine connect inf http ffline
between tags and words, there‘s table 1.
between tags, there‘s table 2.
combine the two tables, p(...) to get the results.
MRF: factors of the tables not necessarily probabilities
BN: must be probabilities. => BN is easier to learn than MRF
Maximum-Entropy Markov Model (MEMM)
Marginals:
1) forward:
2) Belief:
HMM is generative, modeling joint probability P(x,y)
but tagging just needs P(y|x)
https://cedar.buffalo.edu/~srihari/CSE574/Discriminative-Generative.pdf
Full obervation!
(like the offline SLAM?)
biased! because we only look at local observation.
P(x_2|x_1) can be called \Psi(x_1,x_2)
If Y_1 ~~~~ Y_{n-2} are connected somehow, what should be changed?
How close the model is closed to the truth.
2018 10-708 (CMU) Probabilistic Graphical Models {Lecture 10} [HHM and CRF]
标签:use models ati com combine connect inf http ffline
原文地址:https://www.cnblogs.com/ecoflex/p/10231319.html