[Neural Network] {Université de Sherbrooke} C3: Conditional Random Field

http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html

these characteristics may come from a word. (hand writting data)

sequence of observation => model the joint distribution over the whole sequence


linear chain CRF

usually => iid assumption

but for the adjacent positions in a sequence => linear chain CRF

first term: from x_k

seconde term: from V matrix


context window

three neural network, weighted by a(0) a(-1) a(+1)

alternative: only one NN


computing the partition function

y‘ ≠ y

y_k is the resultant sequence

y‘_k is all the probable sequence

the goal here is to calculate Z(X) in polynomial time (dynamic programming)

if someone gives me y2‘ then we can calculate \alpha_1(y2‘)

https://www.spaces.ac.cn/archives/5542/comment-page-1

advantage function????

a = max x_n

V(s) = max_a Q(a|s)

A(a|s) = Q(a|s) - V(s)


computing marginals


performing classification


factors, sufficient statistics and linear CRF


Markov network


factor graph

another visualization to get rid of the ambiguity.


belief propagation

原文地址:https://www.cnblogs.com/ecoflex/p/10884617.html

时间: 2024-10-18 18:34:30

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