A model for handling approximate, noisy or incomplete labeling in text classification

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 681-688

  Ramakrishnan, Ganesh ^a   Chitrapura, Krishna Prasad ^a   Krishnapuram, Raghu ^a   Bhattacharyya, Pushpak ^b  

^a IBM INDIA RESEARCH LAB   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMPUTER NETWORKS; ERROR ANALYSIS; ITERATIVE METHODS; LEARNING SYSTEMS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; SET THEORY;

BAYESIAN MODEL; EM ITERATIONS; INCOMPLETE LABELING; TEXT CLASSIFICATION;

TEXT PROCESSING;

EID: 31844433634     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102437     Document Type: Conference Paper

References (16)

1. Amari, S.-I. (1995). Information geometry of the EM and em algorithms for neural networks. Neural Networks, 8, 1379-1408
2. Ando, R. K., & Zhang, T. (2004). A framework for learning predictive structures from multiple tasks and unlabeled data. Technical Report RC23462, IBM T.J. Watson Research Center.
3. Brodley, C. E., & Friedl, M. A. (1996). Identifying and eliminating mislabeled training instances. Proceedings of the 13th National Conference on Artificial Intelligence (pp. 799-805). Portland, Oregon.
4. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1976). Maximum likelihood from incomplete data via the EM algorithm. Proceedings of the Royal Statistical Society, 1-38.
5. Domingos, P. (1999). Metacost: A general method for making classifiers cost-sensitive. Proceedings of the fifth A CM SIGKDD international conference on Knowledge discovery and data mining (pp. 155-164). New York, NY, US.
6. Griffiths, T. L., & Tenenbaum, J. B. (2001). Exploiting weak prior knowledge in bayesian parameter estimation. Proceedings of NIPS.
7. Hofmann, T. (1999). Probabilistic latent semantic indexing. Proceedings of the 22nd International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 50-57). Berkeley, US.
8. Joachims, T. (1998). Text categorization with support vector machines: learning with many relevant features, Proceedings of 10th European Conference on Machine Learning (pp. 137-142). Chemnitz, DE: Springer Verlag, Heidelberg, DE.
9. Lee, W. S., & Liu, B. (2003). Learning with positive and unlabeled examples using weighted logistic regression. Proceedings of the 20th International Conference on Machine Learning (pp. 448-455). Washington DC, US.
10. Lewis, D. (1998). Naive (bayes) at forty. The independence assumption in information retrieval. Proceedings of 10th European Conference on Machine Learning (pp. 137-142).
11. McCallum, A., & Nigam, K. (1998). A comparison of event models for naive bayes text classification. Proceedings of AAAI-98 Workshop on Learning for Text Categorization.
12. McCallum, A. K. (1996). Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. http://www.cs.cmu.edu/~mccallum/ bow.
13. Nigam, K., Lafferty, J., & McCallum, A. (1999). Using maximum entropy for text classification. Proceedings of Workshop on Machine Learning for Information Filtering, IJCAI, 61-67.
14. Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning., 39, 103-134.
15. Schwaighofer, A. (2002). SVM toolbox for matlab. http://www.igi.tugraz. at/aschwaig/software.html.
16. Yang, Y. (1999). An evaluation of statistical approaches .to text categorization. Information Retrieval, 1, 69-90.