A bayesian logistic regression model for active relevance feedback

ACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings

Volumn , Issue , 2008, Pages 227-234

  Xu, Zuobing ^a   Akella, Ram ^a  

^a University of California Santa Cruz   (United States)

Author keywords

Algorithms

Indexed keywords

ALGORITHMS; BAYESIAN NETWORKS; CONTROL THEORY; FEEDBACK; INFORMATION RETRIEVAL; INFORMATION RETRIEVAL SYSTEMS; INFORMATION SERVICES; LEARNING SYSTEMS; REGRESSION ANALYSIS; RESEARCH AND DEVELOPMENT MANAGEMENT;

ACTIVE LEARNING; ACTIVE-LEARNING ALGORITHMS; BAYESIAN; COMPACT SETS; CRITICAL FACTORS; DATA-SETS; FEATURE SPACES; FEEDBACK DOCUMENTS; HUMAN EVALUATIONS; LOGISTIC REGRESSION ALGORITHMS; LOGISTIC REGRESSION MODELS; NEW APPROACHES; NON-RELEVANT DOCUMENTS; OVER FITTING PROBLEMS; OVER FITTINGS; RELEVANCE FEEDBACKS; RELEVANT DOCUMENTS; RETRIEVAL PERFORMANCE; TRAINING DATUM; UNLABELED DOCUMENTS; USER EVALUATIONS; VARIANCE REDUCTION APPROACHES; VARIANCE REDUCTIONS;

LEARNING ALGORITHMS;

EID: 57549100649     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390334.1390375     Document Type: Conference Paper

References (25)

1. The lemur toolkit, http://www.lemurproject.org.
2. D. A. Cohn, Z. Ghahramani, and M. I. Jordan. Active learning with statistical models. In Advances in Neural Information Processing Systems, volume 7, pages 705-712. The MIT Press, 1995.
3. L. D. and G. W. Training text classifiers by uncertainty sampling. In International ACM Conf. on Research and Development in Information Retrieval, 1994.
4. A. Dayanik, D. D. Lewis, D. Madigan, V. Menkov, and A. Genkin. Constructing information prior distributions from domain knowledge in text classfication. In Proceedings of the 28th annual international A CM SIGIR conference on Research and development in information retrieval, 2006.
5. Y. Freund, H. S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28(2-3): 133-168, 1997.
6. A. Genkin, D. Lewis, and D. Madigan. large-scale bayesian logistic regression for text categorization. Technical report, DIMACS, 2004.
7. D. Harman. Relevance feedback revisited. In Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1-10, 1992.
8. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data mining, Inference and Prediction. Spinger, 2001.
9. S. Hoi, R. Jin, J. Zhu, and M. Lyu. Batch mode active learning and its application to medical image classification. In proceedings of the 23rd international conference on machine learning, 2006.
10. S. C. H. Hoi, R. Jin, and M. R. Lyu. Large scale text categorization by batch mode active learning. In Proceedings of International World Wide Web Conference, 2006.
11. R. Kass, L. Tierney, and J. Kadane. validity of posterior expansions based on laplace's method. Bayesian and likelihood methods in statistics and econometrics, 1990.
12. S. Kay. Fundamentals of statistical signal processing. Prentice-Hall, 1993.
13. R. Kohavi and D. Sommerfield. Feature subset selection using the wrapper method: Overfitting and dynamic search space topology. In The First International Conference on Knowledge Discovery and Data Mining (KDD), 1995.
14. J. Rocchio. Relevance feedback in information retrieval, In The Smart Systemexperiments in automatic document processing. Prentice Hall, Englewood Cliffs, NJ, 1971.
15. N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. In Proc. 18th International Conf. on Machine Learning, pages 441-448. Morgan Kaufmann, San Francisco, CA, 2001.
16. M. Saar-Tsechansky and F. Provost. Active sampling for class probability estimation and ranking. Machine learning, pages 153-178, 2004.
17. G. Salton and C. Buckley. Improving retrieval performance by relevance feedback. Journal of the American Society for Information Science, 41 (4): 133-168, 1990.
18. H. S. Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proceedings of the fifth annual workshop on Computational learning theory, 1992.
19. X. Shen and C. Zhai. Active feedback in ad hoc information retrieval. In Proceedings of the 28th annual international A CM SIGIR conference on Research and development in information retrieval, pages 55-66, March 2005.
20. R. Sutton and A. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, 1998.
21. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In Proceedings of 17th International Conference on Machine Learning, pages 999-1006, 2000.
22. Z. Xu, R. Akella, and Y. Zhang. Incorporating diversity and density in active learning for relevance feedback. In 29th European Conference on Information Retrieval (ECIR), 2007.
23. C. Zhai and J. Lafferty. Model-based feedback in the language modeling approach to information retrieval. In Proceedings of the Tenth ACM International Conference on Information and Knowledge Management, pages 403-410, 2001.
24. T. Zhang and F. J. Oles. A probability analysis on the value of unlabeled data for classification problems. In International Conference on Machine Learning, pages 1191-1198, 2000.
25. Y. Zhang, W. Xu, and J. Callan. Exploration and exploitation in adaptive filtering based on bayesian active learning. In Proceedings of 20th International Conf. on Machine Learning, pages 896-903, 2003.