Discriminative Learning for Label Sequences via Boosting (2002)
by
Yasemin Altun
,
Thomas Hofmann
,
Mark Johnson
| Venue: | Advances in Neural Information Processing Systems 15 |
| Citations: | 25 - 6 self |
BibTeX
@INPROCEEDINGS{Altun02discriminativelearning,
author = {Yasemin Altun and Thomas Hofmann and Mark Johnson},
title = {Discriminative Learning for Label Sequences via Boosting},
booktitle = {Advances in Neural Information Processing Systems 15},
year = {2002},
pages = {977--984},
publisher = {MIT Press}
}
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Abstract
This paper investigates a boosting approach to discriminative learning of label sequences based on a sequence rank loss function.
Citations
| 896 | Additive logistic regression: a statistical view of boosting - Friedman, Hastie, et al. - 2000 |
| 598 | Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge Univ - Durbin, Eddy, et al. - 2000 |
| 220 | Discriminative Reranking for Natural Language Parsing - Collins - 2000 |
| 60 | Ranking algorithms for named-entity extraction: Boosting and the voted perceptron - Collins - 2002 |
| 45 | Algorithms for maximum-likelihood logistic regression - Minka - 2001 |
| 32 | Improved boosting algorithms using con predictions - Schapire, Singer - 1998 |
| 13 | Conditional random Probabilistic models for segmenting and labeling sequence data - Laerty, McCallum, et al. - 2001 |
| 1 | An alternative objective function for Markovian - Kakade, Teh, et al. - 2002 |
