@inproceedings{5fa280b037084b018b4a3a873e149f7f,
title = "Training Conditional Random Fields for Maximum Labelwise Accuracy",
abstract = "We consider the problem of training a conditional random field (CRF) to maximize per-label predictive accuracy on a training set, an approach motivated by the principle of empirical risk minimization. We give a gradient-based procedure for minimizing an arbitrarily accurate approximation of the empirical risk under a Hamming loss function. In experiments with both simulated and real data, our optimization procedure gives significantly better testing performance than several current approaches for CRF training, especially in situations of high label noise.",
author = "Gross, {Samuel S.} and Do, {Chuong B.} and Olga Russakovsky and Serafim Batzoglou",
note = "Publisher Copyright: {\textcopyright} NIPS 2006.All rights reserved; 19th International Conference on Neural Information Processing Systems, NIPS 2006 ; Conference date: 04-12-2006 Through 07-12-2006",
year = "2006",
language = "English (US)",
series = "NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems",
publisher = "MIT Press Journals",
pages = "529--536",
editor = "Bernhard Scholkopf and Platt, {John C.} and Thomas Hofmann",
booktitle = "NIPS 2006",
address = "United States",
}