Combining active learning and dynamic dimensionality reduction

Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Volumn , Issue , 2012, Pages 696-707

  Bilgic, Mustafa ^a  

^a Illinois Institute of Technology   (United States)

Author keywords

Active learning; Classification; Dimensionality reduction; Regularization

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; ITERATIVE METHODS;

ACTIVE LEARNING; CLASSIFICATION MODELS; CURSE OF DIMENSIONALITY; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION TECHNIQUES; DISCRIMINATIVE POWER; LABELED AND UNLABELED DATA; REGULARIZATION;

CLASSIFICATION (OF INFORMATION);

EID: 84880214206     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972825.60     Document Type: Conference Paper

References (22)

1. Josh Attenberg, Prem Melville, and Foster Provost. A unified approach to active dual supervision for labeling features and examples. In European conference on Ma- chine learning and knowledge discovery in databases, pages 40-55, 2010.
2. Richard Bellman and Stuart E Dreyfus. Applied Dynamic Programming. Princeton University Press, 1962.
3. Mustafa Bilgic and Lise Getoor. Value of information lattice: Exploiting probabilistic independence for ef- fective feature subset acquisition. Journal of Artificial Intelligence Research (JAIR), 41:69-95, 2011.
4. Mustafa Bilgic, Lilyana Mihalkova, and Lise Getoor. Active learning for networked data. In Proceedings of the 27th International Conference on Machine Learn- ing (ICML-10), 2010.
5. Thomas M. Cover and Joy A. Thomas. Elements of information theory. Wiley-Interscience, 1991.
6. Gregory Druck, Gideon Mann, and Andrew McCallum. Learning from labeled features using generalized expec- Tation criteria. In ACM SIGIR Conference on Research and Development in Information Retrieval, pages 595- 602, 2008.
7. Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang- Rui Wang, and Chih-Jen Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9:1871-1874, 2008.
8. Aria Haghighi and Dan Klein. Prototype-driven learn- ing for sequence models. In Proceedings of the North American Association for Computational Linguistics (NAACL), pages 320-327, 2006.
9. Mark Hall, Eibe Frank, Georey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. The WEKA data mining software: An update. SIGKDD Explorations, 11, 2009.
10. George H. John, Ron Kohavi, and Karl Peger. Ir- relevant features and the subset selection problem. In International Conference on Machine Learning, pages 121-129, 1994.
11. Su I. Lee, Varun Ganapathi, and Daphne Koller. Efficient structure learning of markov networks using L1-Regularization. In Advances in Neural Information Processing Systems, pages 817-824, 2007.
12. David D. Lewis and William A. Gale. A sequential al- gorithm for training text classiffers. In ACM SIGIR Conference on Research and Development in Informa- Tion Retrieval, pages 3-12, 1994.
13. Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond Mooney. Active feature-value acquisition for classiffer induction. In IEEE Inter- national Conference on Data Mining, pages 483-486, 2004.
14. Andrew Y. Ng. Feature selection, l1 vs. l2 regulariza- Tion, and rotational invariance. In International Con- ference on Machine Learning, 2004.
15. Hema Raghavan, Omid Madani, and Rosie Jones. Active learning with feedback on features and instances. Journal of Machine Learning Research, 7:1655-1686, 2006.
16. Nicholas Roy and Andrew McCallum. Toward optimal active learning through sampling estimation of error reduction. In International Conference on Machine Learning, pages 441-448, 2001.
17. Maytal Saar-Tsechansky and Foster Provost. Active sampling for class probability estimation and ranking. Machine Learning, 54(2):153-178, 2004.
18. Mark Schmidt, Alexandru Niculescu-Mizil, and Kevin Murphy. Learning graphical model structure using l1- regularization paths. In AAAI Conference on Artificial Intelligence, pages 1278-1283, 2007.
19. Burr Settles. Active learning literature survey. Com- puter Sciences Technical Report 1648, University of Wisconsin-Madison, 2009.
20. H. S. Seung, M. Opper, and H. Sompolinsky. Query by committee. In ACM Annual Workshop on Compu- Tational Learning Theory, pages 287-294, 1992.
21. Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B, 58(1):267-288, 1996.
22. Peter D. Turney. Cost-sensitive classi-cation: Empiri- cal evaluation of a hybrid genetic decision tree induc- Tion algorithm. Journal of Arti-cial Intelligence Re- search, 2:369-409, 1995.