1 The maximum-entropy (ME) principle, which prescribes choosing the model that maximizes the entropy out of all models that satisfy given feature constraints, can be seen as a built-in regularization mechanism that avoids overfitting the training data.
it is only a weak regularizer that cannot avoid overfitting in situations where the number of training examples is significantly smaller than the number of features.