Active Bibliography

papers/159 Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data – John Lafferty, Andrew Mccallum, O C. N. Pereira, John Lafferty
442 Shallow Parsing with Conditional Random Fields – Fei Sha, Fernando Pereira - 2003
A Review of Sequential Supervised Learning – Guohua Hao - 2004
1 A review of RKHS methods in machine learning – Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola - 2006
42 A tutorial on energy-based learning – Yann Lecun, Sumit Chopra, Raia Hadsell, Fu Jie Huang, G. Bakir, T. Hofman, B. Schölkopf, A. Smola, B. Taskar (eds - 2006
564 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
Thèse de doctorat de – Télécom Paristech, Spécialité Informatique Et Reseaux, Nataliya Sokolovska, Thierry Artières, Francis Bach, Yves Grandvalet, Marc Tommasi, François Yvon - 2010
5 An introduction to structured discriminative learning – Roland Memisevic - 2006
unknown title – unknown authors
Distributed Generative . . . – n.n.
2 Information Extraction: Methodologies and Applications – Jie Tang, Mingcai Hong, Duo Zhang, Bangyong Liang, Juanzi Li
182 Efficiently Inducing Features of Conditional Random Fields – Andrew McCallum - 2003
1 Learning semantic parsers using statistical syntactic parsing techniques – Ruifang Ge - 2006
49 Conditional random fields: An introduction – Hanna M. Wallach - 2004
9 Maximum Entropy Methods for Biological Sequence Modeling – Eugen C. Buehler, Lyle H. Ungar - 2001
Improving Discriminative Sequential Learning by Discovering Important Association of Statistics – Xuan-hieu Phan, Le-minh Nguyen, Yasushi Inoguchi, Tu-bao Ho
45 Improving Accuracy in Wordclass Tagging through Combination of Machine Learning Systems – Hans Van Halteren, Jakub Zavrel, Walter Daelemans - 2000
35 A Review of Kernel Methods in Machine Learning – Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola - 2006
16 Joint and Conditional Estimation of Tagging and Parsing Models – Mark Johnson - 2001