next up previous contents
Next: About this document ... Up: A Literature Survey on Previous: 8 Ensemble Methods   Contents

Bibliography

Rie Ando and Tong Zhang.
A framework for learning predictive structure from multiple tasks and unlabeled data.
Journal of Machine Learning Research, 6: 1817-1853, November 2005.

Shai Ben-David and Reba Schuller.
Exploiting task relatedness for multiple task learning.
In Proceedings of the 16th Annual Conference on Learning Theory, Washington D.C., USA, August 2003.

Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira.
Analysis of representations for domain adaptation.
In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 137-144. MIT Press, Cambridge, Massachusetts, USA, 2007.

Steffen Bickel and Tobias Scheffer.
Dirichlet-enhanced spam filtering based on biased samples.
In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 161-168. MIT Press, Cambridge, Massachusetts, USA, 2007.

Steffen Bickel, Michael Brückner, and Tobias Scheffer.
Discriminative learning for differing training and test distributions.
In Proceedings of the 24th Annual International Conference on Machine Learning, pages 81-88, Corvallis, Oregon, USA, June 2007.

John Blitzer, Ryan McDonald, and Fernando Pereira.
Domain adaptation with structural correspondence learning.
In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 120-128, Sydney, Australia, July 2006.

John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman.
Learning bounds for domain adaptation.
In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20. MIT Press, Cambridge, Massachusetts, USA, 2008.

Rich Caruana.
Multitask learning.
Machine Learning, 28 (1): 41-75, July 1997.

Yee Seng Chan and Hwee Tou Ng.
Estimating class priors in domain adaptation for word sense disambiguation.
In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pages 89-96, Sydney, Australia, July 2006.

Yee Seng Chan and Hwee Tou Ng.
Word sense disambiguation with distribution estimation.
In Proceedings of the 19th International Joint Conference on Artificial Intelligence, pages 1010-1015, Edingurgh, Scotland, July 2005.

Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien, editors.
Semi-Supervised Learning.
MIT Press, 2006.

Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer.
SMOTE: Synthetic minority over-sampling technique.
Journal of Artificial Intelligence Research, 16: 321-357, June 2002.

Ciprian Chelba and Alex Acero.
Adaptation of maximum entropy capitalizer: Little data can help a lot.
In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pages 285-292, Barcelona, Spain, July 2004.

Wenyuan Dai, Gui-Rong Xue, Qiang Yang, and Yong Yu.
Transferring naive bayes classifiers for text classification.
In Proceedings of the 22nd AAAI Conference on Artificial Intelligence, pages 540-545, Vancouver, British Columbia, Canada, July 2007a.

Wenyuan Dai, Qiang Yang, Gui-Rong Xue, and Yong Yu.
Boosting for transfer learning.
In Proceedings of the 24th Annual International Conference on Machine Learning, pages 193-200, Corvallis, Oregon, USA, June 2007b.

Hal Daumé III.
Frustratingly easy domain adaptation.
In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, pages 256-263, Prague, Czech Republic, June 2007.

Hal Daumé III and Daniel Marcu.
Domain adaptation for statistical classifiers.
Journal of Artificial Intelligence Research, 26: 101-126, May 2006.

Theodoros Evgeniou and Massimiliano Pontil.
Regularized multi-task learning.
In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 109-117, Seattle, Washington, USA, August 2004.

James J. Heckman.
Sample selection bias as a specification error.
Econometrica, 47 (1): 153-161, January 1979.

Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, and Bernhard Schölkopf.
Correcting sample selection bias by unlabeled data.
In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 601-608. MIT Press, Cambridge, MA, 2007.

Nathalie Japkowicz and Shaju Stephen.
The class imbalance problem: A systematic study.
Intelligent Data Analysis, 6 (5): 429-450, November 2002.

Jing Jiang and ChengXiang Zhai.
Instance weighting for domain adaptation in NLP.
In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics, pages 254-271, Prague, Czech Republic, June 2007a.

Jing Jiang and ChengXiang Zhai.
A two-stage approach to domain adaptation for statistical classifiers.
In Proceedings of the ACM 16th Conference on Information and Knowledge Management, pages 401-410, 2007b.

Miroslav Kubat and Stan Matwin.
Addressing the curse of imbalanced training sets: One-sided selection.
In Proceedings of the 14th Annual International Conference on Machine Learning, pages 179-186, Nashville, Tennessee, USA, July 1997.

Xiao Li and Jeff Bilmes.
A Bayesian divergence prior for classifier adaptation.
In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics, San Juan, Puerto Rico, March 2007.

Yi Lin, Yoonkyung Lee, and Grace Wahba.
Support vector machines for classification in nonstandard situations.
Machine Learning, 46 (1-3): 191-202, January 2002.

Charles A. Micchelli and Massimiliano Pontil.
Kernels for multi-task learning.
In Lawrence K. Saul, Yair Weiss, and Léon Bottou, editors, Advances in Neural Information Processing Systems 17, pages 921-928. MIT Press, Cambridge, Massachusetts, USA, 2005.

Kamal Nigam, Andrew K. McCallum, Sebastian Thrun, and Tom Mitchell.
Text classification from labeled and unlabeled documents using EM.
Machine Learning, 39 (2-3): 103-134, May 2000.

Sandeepkumar Satpal and Sunita Sarawagi.
Domain adaptation of conditional probability models via feature subsetting.
In Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 224-235, Warsaw, Poland, September 2007.

Hidetoshi Shimodaira.
Improving predictive inference under covariate shift by weighting the log-likelihood function.
Journal of Statistical Planning and Inference, 90 (2): 227-244, October 2000.

Amos J. Storkey and Masashi Sugiyama.
Mixture regression for covariate shift.
In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 1337-1344. MIT Press, Cambridge, Massachusetts, USA, 2007.

Masashi Sugiyama and Klaus-Robert Müller.
Input-dependent estimation of generalization error under covariate shift.
Statistics & Decisions, 23 (4): 249-279, 2005.

Vladimir N. Vapnik.
The Nature of Statistical Learning Theory.
Springer, second edition, 1999.

Dikan Xing, Wenyuan Dai, Gui-Rong Xue, and Yong Yu.
Bridged refinement for transfer learning.
In Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 324-335, Warsaw, Poland, September 2007.

Ya Xue, Xuejun Liao, Lawrence Carin, and Balaji Krishnapuram.
Multi-task learning for classification with Dirichlet process priors.
Journal of Machine Learning Research, 8: 35-63, May 2007.

Bianca Zadrozny.
Learning and evaluating classifiers under sample selection bias.
In Proceedings of the 21th Annual International Conference on Machine Learning, pages 114-121, Banff, Canada, July 2004.

Jingbo Zhu and Eduard Hovy.
Active learning for word sense disambiguation with methods for addressing the class imbalance problem.
In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 783-790, Prague, Czech Republic, June 2007.

Xiaojin Zhu.
Semi-supervised learning literature survey.
Technical Report 1530, University of Wisconsin-Madison, 2005.



Jing Jiang 2008-03-06