We discuss and analyze the problem of finding a distribution that minimizes the relative entropy to a prior distribution while satisfying max-norm constraints with respect to an ...
Exponential models of distributions are widely used in machine learning for classification and modelling. It is well known that they can be interpreted as maximum entropy models u...
While most popular collaborative filtering methods use low-rank matrix factorization and parametric density assumptions, this article proposes an approach based on distribution-fr...
We propose a framework for modeling sequence motifs based on the maximum entropy principle (MEP). We recommend approximating short sequence motif distributions with the maximum en...
We present a model, based on the maximum entropy method, for analyzing various measures of retrieval performance such as average precision, R-precision, and precision-at-cutoffs....