The EM algorithm ( Dempster et al. , 1977 ) can maximize these functions .
maximum likelihood from incomplete data via the em algorithm
A P Dempster N M Laird D B Rubin
In a log-linear parameterization , for example , a prior that penalizes feature strengths far from 1 can be used to do feature selection and avoid overfitting ( Chen and Rosenfeld , 1999 ) . The EM algorithm ( Dempster et al. , 1977 ) can maximize these functions . Roughly , the E step guesses hidden information : if ( xi , yi ) was generated from the current fÎ¸ , which FST paths stand a chance of having been the path used ?
The EM algorithm ( @@CITATION ) can maximize these functions .