Knowledge transfer via multiple model local structure mapping

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2008, Pages 283-291

  Gao, Jing ^a   Fan, Wei ^b   Jiang, Jing ^a   Han, Jiawei ^a  

^a University of Illinois at Urbana Champaign   (United States)

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

Author keywords

Algorithms

Indexed keywords

CLASSIFICATION ACCURACIES; CLASSIFICATION ALGORITHMS; CLASSIFICATION MODELS; DATA SETS; DIFFERENT DOMAINS; GENERAL APPROACHES; KNOWLEDGE TRANSFERS; LEARNING METHODS; LOCAL STRUCTURES; MULTIPLE MODELS; NEIGHBORHOOD STRUCTURES; OPTIMALITY; PREDICTIVE POWER; SPAM FILTERING; TEST EXAMPLES; TEXT CLASSIFICATIONS; TRAINING EXAMPLES; TRANSFER LEARNING; WEIGHT ASSIGNMENTS;

DATA MINING; EDUCATION; INTRUSION DETECTION; KNOWLEDGE MANAGEMENT; LEARNING SYSTEMS; MINING; MODEL STRUCTURES; TESTING; TEXT PROCESSING;

LEARNING ALGORITHMS;

EID: 65449181688     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1401890.1401928     Document Type: Conference Paper

References (28)

1. C. G. Atkeson, A. W. Moore, and S. Schaal. Locally weighted learning. Artificial Intelligence Review, 11(1-5):11-73, 1997.
2. E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36:105-139, 2004.
3. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representations for domain adaptation. In Proc. of NIPS' 07, pages 137-144. 2007.
4. P. N. Bennett, S. T. Dumais, and E. Horvitz. The combination of text classifiers using reliability indicators. Information Retrieval, 8(1):67-100, 2005.
5. S. Bickel, M. Brückner, and T. Scheffer. Discriminative learning for differing training and test distributions. In Proc. of ICML' 07, pages 81-88, 2007.
6. A. J. Carlson, C. M. Cumby, J. L. R. Nicholas D. Rizzolo, and D. Roth. Snow learning architecture. http://12r.cs.uiuc.edu/"cogcomp/asoftware.php? skey=SNOW#projects.
7. R. Caruana. Multitask learning. Machine Learning, 28(1):41-75, 1997.
8. 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.
9. W. Dai, G.-R. Xue, Q. Yang, and Y. Yu. Co-clustering based classification for out-of-domain documents. In Proc. of KDD' 07, pages 210-219, 2007.
10. W. Dai, Q. Yang, G.-R. Xue, and Y. Yu. Boosting for transfer learning. In Proc. of ICML' 07, pages 193-200.
11. H. Daumé III and D. Marcu. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 26:101-126, 2006.
12. T. Dietterich. Ensemble methods in machine learning. In Proc. of MCS '00, pages 1-15, 2000.
13. W. Fan. Systematic data selection to mine concept-drifting data streams. In Proc. KDD' 04, pages 128-137, 2004.
14. W. Fan and I. Davidson. On sample selection bias and its efficient correction via model averaging and unlabeled examples. In Proc. of SDM'07.
15. J. Gao, W. Fan, and J. Han. On appropriate assumptions to mine data streams: Analysis and practice. In Proc. ICDM' 07, pages 143-152, 2007.
16. A. Genkin, D. D. Lewis, and D. Madigan. Bbr: Bayesian logistic regression software. http://stat.rutgers.edu/~madigan/BBR/.
17. J. Hoeting, D. Madigan, A. Raftery, and C. Volinsky. Bayesian model averaging: a tutorial. Statist. Sci., 14:382-417, 1999.
18. J. Huang, A. J. Smola, A. Gretton, K. M. Borgwardt, and B. Schölkopf. Correcting sample selection bias by unlabeled data. In Proc. of NIPS' 06, pages 601-608. 2007.
19. R. Jacobs, M. Jordan, S. Nowlan, and G. Hinton. Adaptive mixtures of local experts. Neural Computation, 3(1):79-87, 1991.
20. T. Joachims. Making large-scale svm learning practical. advances in kernel methods - support vector learning. MIT-Press, 1999.
21. G. Karypis. Chito - family of data clustering software tools. http://glaros.dtc.umn.edu/gkhome/views/cluto.
22. X. Li and J. Bilmes. A Bayesian divergence prior for classifier adaptation. In Proc. of AISTATS' 07, 2007.
23. D. M. Roy and L. P. Kaelbling. Efficient bayesian task-level transfer learning. In Proc. of IJCAI '07.
24. S. Satpal and S. Sarawagi. Domain adaptation of conditional probability models via feature subsetting. In Proc. of ECML/PKDD' 07, pages 224-235, 2007.
25. H. Shimodaira. Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90(2):227-244, 2000.
26. A. Storkey and M. Sugiyama. Mixture regression for covariate shift. In Proc. of NIPS' 06, pages 1337-1344.
27. H. Wang, W. Fan, P. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proc. of KDD'03, pages 226-235, 2003.
28. X. Zhu. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison, 2005.