End-user feature labeling via locally weighted logistic regression

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2011, Pages 1575-1578

  Wong, Weng Keen ^a   Oberst, Ian ^a   Das, Shubhomoy ^a   Moore, Travis ^a   Stumpf, Simone ^b   McIntosh, Kevin ^a   Burnett, Margaret ^a  

^a OREGON STATE UNIVERSITY   (United States)

^b CITY UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

END USERS; FEATURE LABELING; LOGISTIC REGRESSIONS; TEXT DOCUMENT; TRAINING DATA; USER STUDY;

ARTIFICIAL INTELLIGENCE; REGRESSION ANALYSIS;

LEARNING ALGORITHMS;

EID: 80055039801     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Attenberg, J., Melville, P., and Provost, F. 2010. A unified approach to active dual supervision for labeling features and examples. In Proc. ECML, 40-55. Berlin, Heidelberg: Springer-Verlag.
2. Cleveland, W., and Devlin, S. 1988. Locally-weighted regression: An approach to regression analysis by local fitting. J. American Statistical Assn 83(403):596-610.
3. Deng, K. 1998. Omega: On-line Memory-Based General Purpose System Classifier. PhD Dissertation. Carnegie Mellon University, Pittsburgh, PA.
4. Druck, G., Mann, G., and McCallum, A. 2008. Learning from labeled features using generalized expectation criteria. In Proc. SIGIR, 595-602. New York, NY: ACM Press.
5. Lang, K. 1995. Newsweeder: Learning to filter netnews. In Proc. ICML, 331-339. San Mateo, CA: Morgan Kaufmann.
6. Lewis, D. 2004. Reuters-21578. Available at http://www.daviddlewis.com/ resoursce/testcollections/reuters21578.
7. Lewis, D. D., Yang, Y., Rose, T., Li, F. 2004. RCV1: A new benchmark collection for text categorization research. JMLR 5: 361-397. http://www.jmlr.org/papers/volume5/lewis04a/lewis04a.pdf.
8. Raghavan, H., Madani, O., and Jones, R. 2006. Active Learning with Feedback on Both Features and Instances. JMLR 7: 1655-1686.
9. Raghavan, H. and Allan, J. 2007. An interactive algorithm for asking and incorporating feature feedback into support vector machines, In Proc. SIGIR, 79-86. New York, NY: ACM.
10. Settles, B. 2009. Active learning literature survey. Technical Report 1648, Department of Computer Science, University of Wisconsin-Madison, Madison, WI.
11. Sindhwani, V., Melville, P., and Lawrence, R. 2009. Uncertainty sampling and transductive experimental design for active dual supervision. In Proceedings of ICML, 953-960. New York, NY: ACM.
12. Stumpf, S. Rajaram, V., Li, L., Wong, W.-K., Burnett, M., Dietterich, T., Sullivan, E., and Herlocker J. 2009. Interacting meaningfully with machine learning systems: Three experiments. Int. J. Human-Computer Studies 67(8):639-662.
13. Wong, W-K., Oberst, I., Das, S., Moore, T., Stumpf, S., McIntosh, K., and Burnett, M. 2011. End-user feature labeling: A locally-weighted regression approach. In Proc. Int. Conf. Intelligent User Interfaces, 115-124. New York, NY: ACM.