Large margin transductive transfer learning

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2009, Pages 1327-1336

  Quanz, Brian ^a   Huan, Jun ^a  

^a University of Kansas   (United States)

Author keywords

Kernel method; Large margin classifier; Transductive learning; Transfer learning

Indexed keywords

DATA SEPARATION; DATA SETS; EFFICIENT ALGORITHM; FEATURE SPACE; KERNEL METHODS; LABELED TRAINING DATA; LARGE MARGIN CLASSIFIERS; RECENT STATE; TESTING DATA; TRAINING AND TESTING; TRANSDUCTIVE LEARNING; TRANSFER LEARNING;

CLASSIFIERS; GEARS; KNOWLEDGE MANAGEMENT; MULTILAYER NEURAL NETWORKS; SUPPORT VECTOR MACHINES; VECTOR SPACES;

LEARNING ALGORITHMS;

EID: 74549127565     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1645953.1646121     Document Type: Conference Paper

References (41)

1. R. K. Ando, T. Zhang, and P. Bartlett. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6:1817-1853, 2005.
2. A. Arnold, R. Nallapati, and W. W. Cohen. Exploiting feature hierarchy for transfer learning in named entity recognition. In 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL:HLT), June 2008.
3. M. Belkin, P. Niyogi, V. Sindhwani, and P. Bartlett. Manifold regularization: A geometric framework for learning from examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
4. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representations for domain adaptation. In Advances in Neural Information Processing Systems 20, Cambridge, MA, 2007. MIT Press.
5. S. Bickel, C. Sawade, , and T. Scheffer. Transfer learning by distribution matching for targeted advertising. In Proceedings of the Advances in Neural Information Processing Systems, 2008.
6. S. Bickel. Ecml-pkdd discovery challenge 2006 overview. In Proc. ECML/PKDD Discovery Challenge Workshop, 2006.
7. S. Bickel, M. Brückner, and T. Scheffer. Discriminative learning for differing training and test distributions. In Proc. of the 24th Int. Conf. on Machine Learning (ICML), pages 81-88, 2007.
8. A. Carlson, C. Cumby, J. Rosen, N. Rizzolo, and D. Roth. The snow learning architecture. Software available at http://l2r.cs.uiuc.edu/~cogcomp/ asoftware.php?skey=SNOW .
9. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/ libsvm.
10. O. Chapelle, B. Schölkopf, and A. Zien, editors. Semi-Supervised Learning (Adaptive Computation and Machine Learning). The MIT Press, September 2006.
11. C. Chelba and A. Acero. Adaptation of maximum entropy capitalizer: Little data can help a lot. Computer Speech and Language, 20(4):382-399, 2006.
12. H. Daumé III and D. Marcu. Domain adaptation for statistical classifers. Journal of Artificial Intelligence Research, 26:101-126, 2006.
13. J.-L. Faulon, M. Misra, S. Martin, K. Sale, and R. Sapra. Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics, 24(2):225-233, 2008.
14. J. Gao, W. Fan, J. Jiang, and J. Han. Knowledge transfer via multiple model local structure mapping. In Proceedings of the 14th ACM SIGKDD conference on Knowledge Discovery and Data Mining, 2008.
15. A. Genkin, D. D. Lewis, and D. Madigan. BBR: Bayesian Logistic Regression Software. Software available at http://www.stat.rutgers.edu/ ~madigan/BBR/.
16. M. Grant and S. Boyd. CVX: Matlab software for disciplined convex programming, December 2008. Web page and software available at http://stanford.edu/~boyd/cvx.
17. M. Grant and S. Boyd. Graph implementations for nonsmooth convex programs. Lecture Notes in Control and Information Sciences, 371:95-110, 2008.
18. A. Gretton, K. M. Borgwardt, M. Rasch, B. Scholkopf, and A. J. Smola. A kernel method for the two-sample-problem. In Advances in NIPS 19. MIT Press, 2007.
19. C. Hsieh, K. Chang, C. Lin, S. Keerthi, and S. Sundararajan. A Dual Coordinate Descent Method for Large-scale Linear SVM. In Proceedings of the Twenty Fifth International Conference on Machine Learning (ICML), 2008.
20. J. Huan, W. Wang, and J. Prins. Efficient mining of frequent subgraph in the presence of isomorphism. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM), pages 549-552, 2003.
21. J. Huang, A. Smola, A. Gretton, K. M. Borgwardt, and B. Schölkopf. Correcting sample selection bias by unlabeled data. In Proceedings of Twentieth Annual Conference on Neural Information Processing Systems, 2006.
22. L. Jacob, B. Hoffmann, V. Stoven, and J.-P. Vert. Virtual screening of gpcrs: an in silico chemogenomics approach. Technical Report HAL-00220396, French Center for Computational Biology, 2008.
23. T. Joachims. Transductive inference for text classification using support vector machines. In I. Bratko and S. Dzeroski, editors, Proceedings of ICML-99, 16th International Conference on Machine Learning, pages 200-209. Morgan Kaufmann Publishers, 1999.
24. T. Joachims. Training linear svms in linear time. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 217-226. ACM New York, NY, USA, 2006.
25. H. Larochelle, D. Erhan, and Y. Bengio. Zero-data learning of new tasks. In AAAI, 2008.
26. X. Ling, W. Dai, G. Xue, Q. Yang, and Y. Yu. Spectral domain-transfer learning. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM New York, NY, USA, 2008.
27. Y. Okuno, J. Yang, K. Taneishi, H. Yabuuchi, and G. Tsujimoto. GLIDA: GPCR-ligand database for chemical genomic drug discovery. Nucleic Acids Res., 2006(9).
28. S. J. Pan, J. T. Kwok, and Q. YangPan. Transfer learning via dimensionality reduction. In Proceedings of the 23rd AAAI Conference on Artificial Intelligence, pages 677-682, 2008.
29. B. Quanz and C. Tsatsoulis. Determining object safety using a multiagent, collaborative system. In Environment-Mediated Coordination in Self-Organizing and Self-Adaptive Systems (ECOSOA 2008) Workshop, Venice, Italy, October 2008.
30. R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng. Self-taught learning: transfer learning from unlabeled data. In Proceedings of the 24th international conference on Machine learning, pages 759-766, New York, NY, USA, 2007.
31. S. Satpal and S. 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, 2007.
32. J. Shawe-Taylor and N. Cristianini. Kernel methods for pattern analysis. Cambridge University Press, 2004.
33. H. Shimodaira. Improving predictive inference under convariance shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90(18):227-244, 2000.
34. A. Smalter, J. Huan, and G. Lushington. Structure-based pattern mining for chemical compound classification. In Proceedings of the 6th Asia Pacific Bioinformatics Conference, 2008.
35. M. Sugiyama, S. Nakajima, H. Kashima, P. V. Buenau, and M. Kawanabe. Direct importance estimation with model selection and its application to covariate shift adaptation. In NIPS, 2007.
36. V. Vapnik. Statistical Learning Theory. John Wiley and Sons, 1998.
37. Q. Y. Wenyuan Dai, Gui-Rong Xue and Y. Yu. Co-clustering based classification for out-of-domain documents. In Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 210-219, San Jose, California, USA, August 2007. ACM.
38. Y. Wu and Y. Liu. Robust truncated hinge loss support vector machines. Journal of the American Statistical Association, 102(479):974-983, 2007.
39. Y. Xue, D. Dunson, and L. Carin. The matrix stick-breaking process for flexible multi-task learning. In ICML, 2007.
40. X. Zhu. Semi-supervised learning literature survey. Technical report, Department of Computer Science, University of Wisconsin, Madison, 2008.
41. X. Zhu, T. M. Khoshgoftaar, I. Davidson, and S. Zhang. Editorial: Special issue on mining low-quality data. Knowledge and Information Systems, 11:131-6, 2007.