For example , in ordinary HMM training , xi = E * and represents a completely hidden state sequence ( cfXXX Ristad ( 1998 ) , who allows any regular set ) , while yi is a single string representing a completely observed emission sequence .11 What to optimize ?
hidden markov models with finite state supervision in
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Samples need not be fully observed ( partly supervised training ) : thus xi C E * , yi C A * may be given as regular sets in which input and output were observed to fall . For example , in ordinary HMM training , xi = E * and represents a completely hidden state sequence ( cfXXX Ristad ( 1998 ) , who allows any regular set ) , while yi is a single string representing a completely observed emission sequence .11 What to optimize ? Maximum-likelihood estimation guesses 0Ë to be the 0 maximizing Hi fÎ¸ ( xi , yi ) .
For example , in ordinary HMM training , xi = E * and represents a completely hidden state sequence ( cfXXX @@CITATION , who allows any regular set ) , while yi is a single string representing a completely observed emission sequence .11 What to optimize ?