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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... (Update)
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BibTeX entry: (Update)
S. Della Pietra, V. Della Pietra, and J. Lafferty, "Inducing features of random fields," In IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 380-393, April 1997. http://citeseer.ist.psu.edu/dellapietra95inducing.html More
@article{ pietra97inducing,
author = "Della Pietra, Stephen and Della Pietra, Vincent J. and John D. Lafferty",
title = "Inducing Features of Random Fields",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
volume = "19",
number = "4",
pages = "380-393",
year = "1997",
url = "citeseer.ist.psu.edu/dellapietra95inducing.html" }
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