Predictive modeling with heterogeneous sources

Proceedings of the 10th SIAM International Conference on Data Mining, SDM 2010

Volumn , Issue , 2010, Pages 814-825

  Shi, Xiaoxiao ^a   Liu, Qi ^b   Fan, Wei ^c   Yang, Qiang ^d   Yu, Philip S ^a  

^a University of Illinois at Chicago   (United States)

^b TONGJI UNIVERSITY   (China)

^c IBM T J WATSON RESEARCH CENTER   (United States)

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

Author keywords

Heterogeneous learning; Predictive model; Transfer learning

Indexed keywords

VACCINES; VIRUSES;

CLASSIFICATION TASKS; DIFFERENT DISTRIBUTIONS; GENERALIZATION BOUND; HETEROGENEOUS LEARNING; HETEROGENEOUS SOURCES; MARGINAL DISTRIBUTION; PREDICTIVE MODELING; TRANSFER LEARNING;

DATA MINING;

EID: 84871358217     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972801.71     Document Type: Conference Paper

References (20)

1. C. C. Aggarwal, P. S. Yu, A Survey of Uncertain Data Algorithms and Applications. TKDE, 21(5) 609-623 (2009)
2. S. An, W. Liu, and S. Venkatesh, Face recognition using kernel ridge regression. In: CVPR 07 (2007)
3. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira, Analysis of representations for domain adaptation. In: NIPS 07 (2007)
4. S. Bickel, J. Bogojeska, T. Lengauer, and T. Scheffer, Multi-task learning for hiv therapy screening. In: ICML 08 (2008)
5. J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman, Learning Bounds for Domain Adaptation. In: NIPS 07 (2007)
6. R. Caruana, Multitask learning. Machine Learning 28(1) 41-75 (1997)
7. J. Gao, W. Fan, Y. Sun, and J. Han, Heterogeneous source consensus learning via decision propagation and negotiation. In: KDD 09 (2009)
8. J. W. Klingelhoefer, L. Moutsianas, and C. Holmes, Approximate bayesian feature selection on a large meta-dataset offers novel insights on factors that effect sirna potency. Bioinformatics, 25, 1594-1601 (2009)
9. N. D. Lawrence, and J. C. Platt: Learning to learn with the informative vector machine. In: ICML 04 (2004)
10. Y. Ren, W. Gong, Q. Xu, X. Zheng, D. Lin, Y. Wang, and T. Li, siRecords: an extensive database of mammalian siRNAs with efficacy ratings. Bioinformatics, 22(8), 1027-1028 (2006)
11. D. Shen, J. Sun, Q. Yang, and Z. Chen, Building bridges for web query classification. In: SIGIR 06 (2006)
12. X. Shi, W. Fan, and J. Ren, Actively Transfer Domain Knowledge. In: ECML/PKDD 08 (2008)
13. X. Shi, W. Fan, Q. Yang, and J. Ren, Relaxed Transfer of Different Classes via Spectral Partition. In: ECML/PKDD 09 (2009)
14. P. Sætrom, and O. Snøve, A comparison of siRNA efficacy predictors. Biochemical and Biophysical Research Communications, 321(1), 247-253 (2004)
15. H. Tafer, S. Ameres, G. Obernosterer, C. Gebeshuber, R. Schroeder, J. Martinez, and I. Hofacker, The impact of target site accessibility on the design of effective siRNAs. Nature Biotechnology, 26(5), 578C583 (2008)
16. S. Takasaki, Y. Kawamura, and A. Konagaya, Selecting effective siRNA sequences by using radial basis function network and decision tree learning. BMC bioinformatics, 7(Suppl 5), S22 (2006)
17. P. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. Pearson International Edition (2005)
18. B. Wang, J. Tang, W. Fan, S. Chen, Z. Yang, and Y. Liu Heterogeneous Cross Domain Ranking in Latent Space. In: CIKM 09 (2009)
19. Q. Yang, Y. Chen, G. Xue, W. Dai, and Y. Yu, Heterogeneous Transfer Learning for Image Clustering via the Social Web. In: ACL and AFNLP 09 (2009)
20. X. Zhu, Semi-Supervised Learning Literature Survey. Computer Sciences TR 1530, University of Wisconsin Madison (2008)