Text classification from positive and unlabeled documents

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2003, Pages 232-239

  Yu, Hwanjo ^a   Zhai, ChengXiang ^a   Han, Jiawei ^a  

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

Author keywords

Machine Learning; SVM; Text Classification; Text Filtering

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATABASE SYSTEMS; ITERATIVE METHODS; LEARNING SYSTEMS; MATHEMATICAL OPERATORS; MATHEMATICAL TRANSFORMATIONS; POLYNOMIALS; PROBLEM SOLVING; SET THEORY; THEOREM PROVING;

SUPPORT VECTOR MACHINES (SVM); TEXT CLASSIFICATION; TEXT FILTERING; UNLABELED DOCUMENTS;

INFORMATION RETRIEVAL;

EID: 18744413274     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/956908.956909     Document Type: Conference Paper

References (18)

1. K. M. A. Chai, H. T. Ng, and H. L. Chieu. Bayesian online classifiers for text classfiers and filtering. In Proc. 25th ACM Int. Conf. on Research and Development in Information Retrieval (SIGIR'02), pages 97-104, Tampere, Finland, 2002.
2. C.-C. Chang and C.-J. Lin. Training nu-support vector classifiers: theory and algorithms. Neural Computation, 13:2119-2147, 2001.
3. T. Joachims. Text categorization with support vector machines. In Proc. 10th European Conference on Machine Learning (ECML'98), pages 137-142, Chemnitz, Germany, 1998.
4. T. Joachims. Transductive inference for text classification using support vector machines. In Proc. 16th Int. Conf. Machine Learning (ICML'00), pages 200-209, Bled, Slovenia, 1999.
5. T. Joachims. A statistical learning model of text classification with support vector machines. In Proceedings of SIGIR-01, 24th ACM Int. Conf. on Research and Development in Information Retrieval, pages 128-136, New Orleans, US, 2001.
6. D. D. Lewis. Representation and Learning in Information Retrieval. PhD thesis, Department of Computer and Information Science, University of Massachusetts, 1992.
7. B. Liu, W. S. Lee, P. S. Yu, and X. Li. Partially supervised classification of text documents. In Proc. 19th Int. Conf. Machine Learning (ICML'02), pages 387-394, Sydney, Australia, 2002.
8. L. M. Manevitz and M. Yousef. One-class SVMs for document classification. Journal of Machine Learning Research, 2:139-154, 2001.
9. A. McCallum, K. Nigam, J. Rennie, and K. Seymore. A machine learning approach to building domain-specific search engines. In The Sixteenth Int. Joint Conf. on Artificial Intelligence (IJCAI-99), Stockholm, Sweden, 1999.
10. K. Nigam. Text classification from labeled and unlabeled documents using EM. Machine Learning, 39:103-134, 2000.
11. B. Scholkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett. New support vector algorithms. Neural Computation, 12:1083-1121, 2000.
12. F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1): 1-47, 2002.
13. D. M. J. Tax and R. P. W. Duin. Uniform object generation for optimizing one-class classifiers. Journal of Machine Learning Research, 2:155-173, 2001.
14. V. N. Vapnik. Statistical Learning Theory. John Wiley and Sons, 1998.
15. Y. Yang. A study on thresholding strategies for text categorization. In Proc. 24th ACM Int. Conf. on Research and Development in Information Retrieval (SIGIR '01), pages 137-145, New Orleans, Louisiana, 2001.
16. Y. Yang and X. Liu. A re-examination of text categorization methods. In Proc. 22th ACM Int. Conf. on Research and Development in Information Retrieval (SIGIR'99), pages 42-49, Berkeley, CA, 1999.
17. H. Yu. SVMC: Single-class classification with support vector machines. In Proc. Int. Joint Conf. on Articial Intelligence (IJCAI-03), Acapulco, Maxico, 2003.
18. H. Yu, J. Han, and K. C. Chang. PEBL: Positive-example based learning for Web page classification using SVM. In Proc. 8th Int. Conf. Knowledge Discovery and Data Mining (KDD'02), pages 239-248, Edmonton, Canada, 2002.