The principle of maximum entropy states that when one searches among probability distributions that model the observed data ( evidence ) , the preferred one is the one that maximizes the entropy ( a measure of the uncertainty of the model ) ( Berger et al. , 1996 ) .
a maximum entropy approach to natural language processing
A Berger S Della Pietra V Della Pietra
We use a maximum entropy Markov model ( MEMM ) classifier . The principle of maximum entropy states that when one searches among probability distributions that model the observed data ( evidence ) , the preferred one is the one that maximizes the entropy ( a measure of the uncertainty of the model ) ( Berger et al. , 1996 ) . One big advantage of this approach is that it can combine arbitrary and diverse types of information in making a classification decision .
The principle of maximum entropy states that when one searches among probability distributions that model the observed data ( evidence ) , the preferred one is the one that maximizes the entropy ( a measure of the uncertainty of the model ) ( @@CITATION ) .