J08-1002 algorithm is proposed for maximum entropy estimation without unpacking feature forests
N04-1039 Khudanpur . 1995 . A method of maximum entropy estimation with relaxed constraints . In
C00-2126 some training data , the maximum entropy estimation process produces a model ill
J10-4016 an artifact of our particular entropy estimation method ? We do not think so .
C00-2126 ) ct i . ( 3 ) Y i The maximum entropy estimation technique guarantees that for
E99-1026 some training data , the maximum entropy estimation process produces a model in which
P01-1039 a set of features the maximum entropy estimation procedure computes a weight parameter
N07-1055 . We are investigating maximum entropy estimation as a solution to this problem
J08-1002 proposed an algorithm for maximum entropy estimation for packed representations of
P02-1025 would be to apply the maximum entropy estimation technique ( MaxEnt ( Berger et
P00-1042 Ell aigi ( h , f f The maximum entropy estimation technique guarantees that for
E99-1026 f1h ) -- Z ( h ) = The maximum entropy estimation technique guarantees that for
P00-1042 some training data , the maximum entropy estimation process produces a model in which
M98-1018 some training data , the maximum entropy estimation process produces a model in which
C04-1204 programming algorithm for maximum entropy estimation ( Miyao and Tsujii , 2002 ; Geman
W02-0401 possible to integrate into maximum entropy estimation ( simple ) conjugate priors that
J12-3007 which extends standard maximum entropy estimation by incorporating hidden dependency
J10-4016 ) using an entirely different entropy estimation method ( see Figure 8 in their
C00-1051 bottom-up model based on maximum entropy estimation . Note that these models were
A00-2013 approximation of the " correct " Maximum Entropy estimation . 3.5 Handling Unknown Words
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