Transfer learning via dimensionality reduction

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2008, Pages 677-682

  Pan, Sinno Jialin ^a   Kwok, James T ^a   Yang, Qiang ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATIONS; ARTIFICIAL INTELLIGENCE; BIONICS; EDUCATION; IMAGE SEGMENTATION; LEARNING SYSTEMS; REGRESSION ANALYSIS; SPEECH RECOGNITION; TARGETS; TEXT PROCESSING;

DIFFERENT DISTRIBUTIONS; DIFFERENT DOMAINS; DIMENSIONALITY REDUCTION METHODS; DIMENSIONALITY REDUCTIONS; DO-MAINS; FEATURE SPACES; LABELED DATUMS; REAL WORLDS; REGRESSION MODELS; TARGET DOMAINS; TEXT CLASSIFICATIONS; TRAINING AND TESTING; TRANSFER LEARNINGS;

LEARNING ALGORITHMS;

EID: 57749201133     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Ando, R. K., and Zhang, T. 2005. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research 6: 1817-1853.
2. Argyriou, A.; Evgeniou, T.; and Pontil, M. 2007. Multitask feature learning. In Proceedings of the 19th Annual Conference on Neural Information Processing Systems. 41-48.
3. Belkin, M.; Niyogi, P.; and Sindhwani, V. 2006. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research 7: 2399-2434.
4. Blitzer, J.; McDonald, R.; and Pereira, F. 2006. Domain adaptation with structural correspondence learning. In Proceedings of the Conference on Empirical Methods in Natural Language, 120-128.
5. Borgwardt, K. M.; Gretton, A.; Rasch, M. J.; Kriegel, H.-P; Schölkopf, B.; and Smola, A. J. 2006. Integrating structured biological data by kernel maximum mean discrepancy. In Proceedings of the 14th International Conference on Intelligent Systems for Molecular Biology, 49-57.
6. Caruana, R. 1997. Multitask learning. Machine Learning 28(1): 41-75.
7. Dai, W.; Yang, Q.; Xue, G.; and Yu, Y. 2007. Boosting for transfer learning. In Proceedings of the 24th International Conference on Machine Learning, 193-200.
8. Huang, J.; Smola, A.; Gretton, A.; Borgwardt, K. M.; and Schölkopf, B. 2007. Correcting sample selection bias by unlabeled data. In Proceedings of the 19th Annual Conference on Neural Information Processing Systems.
9. Lanckriet, G. R. G.; Cristianini, N.; Bartlett, P. L.; Ghaoui, L. E.; and Jordan, M. I. 2004. Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research 5: 27-72.
10. Pan, S. J.; Kwok, J. T.; Yang, Q.; and Pan, J. J. 2007. Adaptive localization in a dynamic WiFi environment through multi-view learning. In Proceedings of the 22nd AAAI Conference on Artificial Intelligence, 1108-1113.
11. Raina, R.; Battle, A.; Lee, H.; Packer, B.; and Ng, A. Y 2007. Self-taught learning: Transfer learning from unlabeled data. In Proceedings of the 24th International Conference on Machine Learning, 759-766.
12. Song, L.; Smola, A.; Borgwardt, K.; and Gretton, A. 2008. Colored maximum variance unfolding. In Proceedings of the 20th Annual Conference on Neural Information Processing Systems.
13. Song, L. 2007. Learning via Hilbert Space Embedding of Distributions. Ph.D. Dissertation, The University of Sydney. Draft.
14. Steinwart, I. 2001. On the influence of the kernel on the consistency of support vector machines. Journal of Machine Learning Research 2: 67-93.
15. Sugiyama, M.; Nakajima, S.; Kashima, H.; Buenau, P. V.; and Kawanabe, M. 2008. Direct importance estimation with model selection and its application to covariate shift adaptation. In Proceedings of the 20th Annual Conference on Neural Information Processing Systems.
16. van der Maaten, L. J. P.; Postma, E. O.; and van den Herik, H. J. 2007. Dimensionality reduction: A comparative review. Published online.
17. Weinberger, K. Q.; Sha, F; and Saul, L. K. 2004. Learning a kernel matrix for nonlinear dimensionality reduction. In Proceedings of the 21st International Conference on Machine Learning.
18. Zhu, X.; Ghahramani, Z.; and Lafferty, J. D. 2003. Semi-supervised learning using gaussian fields and harmonic functions. In Proceeding of The 22th International Conference on Machine Learning, 912-919.