Abstract
We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, t...
We formulate and prove a convex duality theorem for Bregman distances and present a technique based on auxiliary functions for deriving and proving convergence of iterative algorithms to minimize Breg...
We formulate and prove a convex duality theorem for Bregman distances and present a technique based on auxiliary functions for deriving and proving convergence of iterative algorithms to minimize Breg...
We present a class of statistical learning algorithms formulated in terms of minimizing Bregman distances, a family of generalized entropy measures associated with convex functions. The inductive lear...
this paper, we describe a method for statistical modeling based on maximum entropy. We present a maximum-likelihood approach for automatically constructing maximum entropy models and describe how to i...
this paper, we describe a method for statistical modeling based on maximum entropy. We present a maximum-likelihood approach for automatically constructing maximum entropy models and describe how to i...
Abstract
The authors present an algorithm to adapt a n-gram language model to a document as it is dictated. The observed partial document is used to estimate a unigram distribution for the words that...
this paper as a gauntlet thrown down before the computational linguistics community. The Brown Corpus is a widely available, standard corpus and the subject of much linguistic research. By predicting ...