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Inducing features of random fields

Author: Stephen Della Pietra; Vincent Della Pietra; John D Lafferty
Publisher: Pittsburgh, Pa. : School of Computer Science, Carnegie Mellon University, [1995]
Series: Research paper (Carnegie Mellon University. School of Computer Science), CMU-CS-95-144.
Edition/Format:   Book : English
Database:WorldCat
Summary:
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, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy  Read more...
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Document Type: Book
All Authors / Contributors: Stephen Della Pietra; Vincent Della Pietra; John D Lafferty
OCLC Number: 33363094
Notes: "May, 1995."
Description: 34 p. : ill. ; 28 cm.
Series Title: Research paper (Carnegie Mellon University. School of Computer Science), CMU-CS-95-144.
Responsibility: Stephen Della Pietra, Vincent Della Pietra, John Lafferty.

Abstract:

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, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random field models and techniques introduced in this paper differ from those common to much of the computer vision literature in that the underlying random fields are non-Markovian and have a large number of parameters that must be estimated. Relations to other learning approaches including decision trees and Boltzmann machines are given. As a demonstration of the method, we describe its application to the problem of automatic word classification in natural language processing."

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