Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression

Proceedings, Twentieth International Conference on Machine Learning

Volumn 1, Issue , 2003, Pages 448-455

  Lee, Wee Sun ^a   Liu, Bing ^b  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b University of Illinois at Chicago   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LINEAR FUNCTIONS; SUPPORT VECTOR MACHINES (SVM); WEIGHTED LOGISTIC REGRESSION;

CLASSIFICATION (OF INFORMATION); DATA ACQUISITION; ERRORS; PROBABILISTIC LOGICS; REGRESSION ANALYSIS; WEBSITES;

LEARNING SYSTEMS;

EID: 1942516926     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Agrawal, R., Bayardo Jr., R., & Srikant, R. (2000). Athena: Mining-based interactive management of text databases. International Conference on Extending Database Technology.
2. Blum, A., Frieze, A. M., Kannan, R., & Vempala, S. (1996). A polynomial-time algorithm for learning noisy linear threshold functions. IEEE Symposium on Foundations of Computer Science (pp. 330-338).
3. Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. COLT: Proceedings of the Workshop on Computational Learning Theory.
4. Bylander, T. (1994). Learning linear threshold functions in the presence of classification noise. COLT: Proceedings of the Workshop on Computational Learning Theory.
5. Chang, C.-C., & Lin, C.-J. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
6. Denis, F. (1998). PAC learning from positive statistical queries. Proc. 9th International Conference on Algorithmic Learning Theory - ALT '98 (pp. 112-126).
7. Denis, F., Gilleron, R., & Tommasi, M. (2002). Text classification from positive and unlabeled examples. The 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU.
8. Hoffgen, K.-U., Simon, H.-U., & Horn, K. S. V. (1995). Robust trainability of single neurons. Journal of Computer and System Sciences, 50, 114-125.
9. Joachims, T. (1998). Making large-scale svm learning practical. LS8-Report, 24, Universitat Dortmund, LS VIII-Report.
10. Joachims, T. (1999). Transductive inference for text classification using support vector machines. Proceedings of ICML-99, 16th International Conference on Machine Learning (pp. 200-209).
11. Kearns, M. (1998). Efficient noise-tolerant learning from statistical queries. Journal of the ACM, 45, 983-1006.
12. Lang, K. (1995). Newsweeder: Learning to filter net-news. International Conference on Machine Learning (pp. 331-339).
13. Liu, B., Lee, W. S., Yu, P. S., & Li, X. (2002). Partially supervised classification of text documents. International Conf. on Machine Learning (pp. 387-394).
14. Manevitz, L. M., & Yousef, M. (2001). One class SVMs for document classification. Journal of Machine Learning Research, 2, 139-154.
15. Mason, L., Baxter, J., Bartlett, P., & Frean, M. (1999). Boosting algorithms as gradient descent in function space. Technical report, RSISE, Australian National University, 1999..
16. Mitchell, T. (1997). Machine learning. New York: McGraw-Hill.
17. Muggleton, S. (2001). Learning from positive data. Machine learning, to appear.
18. Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (1998). Learning to classify text from labeled and unlabeled documents. AAAI-98 (pp. 792-799). Madison, US: AAAI Press, Menlo Park, US.
19. Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (1999). Estimating the support of high dimensional distribution. Technical Report. Microsoft Research MSR-TR-99-87.
20. Yu, H., Han, J., & Chang, K. C.-C. (2002). PEBL: Positive example based learning for web page classification using SVM. Proc. 2002 Int. Conf. on Knowledge Discovery in Databases (KDD'02).