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 ) .
a gaussian prior for smoothing maximum entropy models
Stanley F Chen Ronald Rosenfeld
Maximum-posterior estimation tries to maximize P ( 0 ) Â· Hi fÎ¸ ( xi , yi ) where P ( 0 ) is a prior probability . 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 .
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 ( @@CITATION ) .