Inducing Features of Random Fields

Inducing Features of Random Fields (1997)

by Stephen Della Pietra , Vincent Della Pietra , John Lafferty

Venue:	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Citations:	464 - 14 self

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Datum	Value	Source
TITLE	Inducing Features of Random Fields	INFERENCE
AUTHOR NAME	Stephen Della Pietra	user correction
AUTHOR NAME	Vincent Della Pietra	user correction
AUTHOR NAME	John Lafferty	user correction
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, are given. As a demonstration of the method, we describe its application to the problem of automatic word classifica...	user correction - Legacy Corrections
YEAR	1997	INFERENCE
VENUE	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE	user correction
VENUE TYPE	JOURNAL	INFERENCE
PAGES	380--393	INFERENCE
VOLUME	19	INFERENCE
NUMBER	4	user correction
CITATIONS	22 found	ParsCit 1.0