12 : Conditional Random Fields
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چکیده
1 Hidden Markov Model 1.1 General parametric form In hidden Markov model (HMM), we have three sets of parameters, transition probability matrix A : p(y t = 1|y t−1 = 1) = ai,j , initialprobabilities : p(y1) ∼ Multinomial(π1, π2, ..., πM ), emission probabilities : p(xt|y t) ∼ Multinomial(bi,1, bi,2, ..., bi,K). 1.2 Inference The inference can be done with forward algorithm which computes α t ≡ μt−1→t(k) = P (x1, ..., xt−1, xt, y t = 1) recursively by α t = p(xt|y t = 1) ∑
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تاریخ انتشار 2014