A comparative study of methods for transductive transfer learning

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2007, Pages 77-82

  Arnold, Andrew ^a   Nallapati, Ramesh ^a   Cohen, William W ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; DATA MINING; DECISION SUPPORT SYSTEMS; INFORMATION MANAGEMENT; MINING; SEARCH ENGINES;

COMPARATIVE STUDIES; CURRENT STATE; FEATURE TRANSFORMATIONS; INTERNATIONAL CONFERENCES; LABELED DATA; MAXIMUM ENTROPY; POSITIVE EXAMPLES; TARGET DOMAIN; TEST DATA; TRANSFER LEARNING;

EDUCATION;

EID: 49549110461     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2007.109     Document Type: Conference Paper

References (20)

1. R. K. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. In JMLR 6, pages 1817 - 1853, 2005.
2. A. L. Berger, S. D. Pietra, and V. J. D. Pietra. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1):39-71, 1996.
3. R. Bunescu, R. Ge, R. Kate, E. Marcotte, R. Mooney, A. Ramani, and Y. Wong. Comparative experiments on learning information extractors for proteins and their interactions. In Journal of AI in Medicine, 2004. Data from ftp://ftp.cs.utexas.edu/pub/mooney/bio-data/proteins.tar.gz.
4. C. Chelba and A. Acero. Adaptation of maximum entropy capitalizer: Little data can help a lot. In D. Lin and D. Wu, editors, EMNLP 2004, pages 285-292. ACL, 2004.
5. W. W. Cohen. Minorthird: Methods for identifying names and ontological relations in text using heuristics for inducing regularities from data. http://minorthird.sourceforge.net, 2004.
6. H. Daumé III. Frustratingly easy domain adaptation. In ACL, 2007.
7. H. Daumé III and D. Marcu. Domain adaptation for statistical classifiers. In Journal of Artificial Intelligence Research 26, pages 101-126, 2006.
8. K. Franzén, G. Eriksson, F. Olsson, L. Asker, P. Lidn, and J. Cöster. Protein names and how to find them. In International Journal of Medical Informatics, 2002.
9. Y. Grandvalet and Y. Bengio. Semi-supervised learning by entropy minimization. In CAP, Nice, France, 2005.
10. K. Ji, M. Ohta, and Y. Tsujii. Tuning support vector machines for biomedical named entity recognition. In ACL Workshop on Natural Language Processing in the Biomedi-cal Domain., 2002.
11. T. Joachims. Transductive inference for text classification using support vector machines. In ICML 16, 1999.
12. T. Joachims. Learning to Classify Text Using Support Vector Machines. Kluwer, 2002.
13. T. Joachims. Transductive learning via spectral graph partitioning. In ICML, 2003.
14. K. Nigam, J. Lafferty, and A. McCallum. Using maximum entropy for text classification, 1999.
15. L. Shi and F. Campagne. Building a protein name dictionary from full text: a machine learning term extraction approach. In BMC Bioinformatics 6:88, 2005.
16. V. Sindhwani, P. Niyogi, and M. Belkin. Beyond the point cloud: from transductive to semi-supervised learning. In ICML, pages 824-831. ACM, 2005.
17. C. Sutton and A. McCallum. Composition of conditional random fields for transfer learning. In HLT/EMLNLP, 2005.
18. V. Vapnik. Statistical Learning Theory. Wiley, 1998.
19. R. C. Wang, A. Tomasic, R. E. Frederking, and W. W. Cohen. Learning to extract gene-protein names from weakly-labeled text in preparation. In preparation, 2006.
20. X. Zhu. Semi-supervised learning literature survey. In Technical Report 1530. University of Wisconsin, 2005.