Statistical Learning Algorithms Based on Bregman Distances

Statistical Learning Algorithms Based on Bregman Distances (1997)

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by John Lafferty , Stephen Della Pietra , Vincent Della Pietra

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Datum	Value	Source
TITLE	Statistical Learning Algorithms Based on Bregman Distances	user correction - Legacy Corrections
AUTHOR NAME	John Lafferty	user correction - Legacy Corrections
AUTHOR AFFIL	Carnegie Mellon University	user correction - Legacy Corrections
AUTHOR NAME	Stephen Della Pietra	user correction - Legacy Corrections
AUTHOR AFFIL	y	user correction - Legacy Corrections
AUTHOR NAME	Vincent Della Pietra	user correction - Legacy Corrections
AUTHOR AFFIL	y; Renaissance Technologies	user correction - Legacy Corrections
ABSTRACT	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 learning scheme is akin to growing a decision tree, with the Bregman distance filling the role of the impurity function in tree-based classifiers. Our approach is based on two components. In the feature selection step, each linear constraint in a pool of candidate features is evaluated by the reduction in Bregman distance that would result from adding it to the model. In the constraint satisfaction step, all of the parameters are adjusted to minimize the Bregman distance subject to the chosen constraints. We introduce a new iterative estimation algorithm for carrying out both the feature selection and constraint satisfaction steps, and outline a proof of the convergence of these algorithms. 1 Introduction In this paper we present a class of statistical learning algorithms formulated in terms...	user correction - Legacy Corrections
YEAR	1997	user correction - Legacy Corrections
CITATIONS	11 found	ParsCit 1.0