Learning to rank with partially-labeled data

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

Volumn , Issue , 2008, Pages 251-258

  Duh, Kevin ^a   Kirchhoff, Katrin ^a  

^a University of Washington   (United States)

Author keywords

Boosting; Information retrieval; Kernel principal components analysis; Learning to rank; Transductive learning

Indexed keywords

EDUCATION; INFORMATION RETRIEVAL; INFORMATION RETRIEVAL SYSTEMS; INFORMATION SERVICES; MANAGEMENT INFORMATION SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY DENSITY FUNCTION; RESEARCH AND DEVELOPMENT MANAGEMENT;

BOOSTING; KERNEL PRINCIPAL COMPONENTS ANALYSIS; LABELED DATUMS; LEARNING TO RANK; LEARNING TO RANKS; OHSUMED; RANKING ALGORITHMS; RANKING FUNCTIONS; RANKING PERFORMANCES; TEST DATUMS; TRANSDUCTIVE LEARNING; TRANSDUCTIVE LEARNINGS;

LEARNING ALGORITHMS;

EID: 57349182209     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390334.1390379     Document Type: Conference Paper

References (37)

1. S. Agarwal. Ranking on graph data. In ICML, 2006.
2. R. K. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. In Journal of Machine Learning Research, volume 6, 2005.
3. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representations for domain adaptation. In Neural Information Processing Systems, 2006.
4. C. Burges, R. Ragno, and Q. Le. Learning to rank with nonsmooth cost functions. In NIPS, 2006.
5. C Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proc. of the 22nd International Conference on Machine Learning, 2005.
6. W. Chu and Z. Ghahramani. Extensions of gaussian processes for ranking: semi-supervised and active learning. In NIPS Wksp on Learning to Rank, 2005.
7. K. Crammer and Y. Singer. Pranking with ranking. In Neural Information Processing Systems (NIPS), 2001.
8. Y. Freund, R. Iyer, R. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4, 2003.
9. X. Geng, T.-Y. Liu, T. Qin, and H. Li. Feature selection for ranking. In SIGIR, 2007.
10. J. He, M. Li, H.-J. Zhang, H. Tong, and C. Zhang Manifold-ranking based image retrieval. In ACM Conf. on Multimedia, 2004.
11. R. Herbrich, T. Graepel, and K. Obermayer. Advances in Large Margin Classifiers, chapter Large margin rank boundaries for ordinal regression. MIT Press, 2000.
12. K. Järvelin and J. Kekäläinen. IR evaluation methods for retrieving highly relevant documents. In SIGIR, 2000.
13. T. Joachims. Optimizing search engines using clickthrough data. In SIGKDD, 2003.
14. J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), 1999.
15. I. Kondor and J. Lafferty. Diffusion kernels on graphs and other discrete structures. In ICML, 2002.
16. J. H. Lee. Analysis of multiple evidence combination. In SIGIR, 1997.
17. P. Li, C. Burges, and Q. Wu. McRank: Learning to rank using classification and gradient boosting. In NIPS, 2007.
18. T.-Y. Liu, T. Qin, J. Xu, W. Xiong, and H. Li. LETOR: Benchmark dataset for research on learning to rank for information retrieval. In SIGIR Workshop on Learning to Rank for IR (LR4IR), 2007.
19. L. Mason, J. Baxter, P. Bartless, and M. Frean. Boosting as gradient descent. In NIPS, 2000.
20. D. Metzler. Direct maximization of rank-based metrics. Technical report, University of Massachusetts, Amherst CIIR, 2005.
21. R. Raina, A. Battle, H. Lee, B. Packer, and A. Ng. Self-taught learning: transfer learning from unlabeled data. In ICML, 2007.
22. S. Robertson. Overview of the Okapi projects. Journal of Documentation, 53(1), 1997.
23. R. E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3), 1999.
24. B. Schölkopf, A. Smola, and K.-R. Müller. Nonlinear component analysis as kernel eigenvalue problem. Neural Computation, 10, 1998.
25. J. Shawe-Taylor and N. Cristianini. Kernel methods for pattern analysis. Cambridge Univ. Press, 2004.
26. A. Smola, O. Mangasarian, and B. Schölkopf. Sparse kernel feature analysis. Technical Report 99-03, University of Wisconsin, Data Mining Institute, 1999.
27. T. Truong, M.-R. Amini, and P. Gallinari. Learning to rank with partially labeled training data. In Int'l Conf. on Multidisciplinary Info Sci/Tech, 2006.
28. M.-F. Tsai, T.-Y. Liu, T. Qin, H.-H. Chen, and W.-Y. Ma. FRank: a ranking method with fidelity loss. In SIGIR, 2007.
29. J. Wang, M. Li, Z. Li, and W.-Y. Ma. Learning ranking function via relevance propagation. Technical report, Microsoft Research Asia, 2005.
30. J. Weston, R. Kuang, C. Leslie, and W. S. Noble. Protein ranking by semi-supervised network propagation. BMC Bioinformatics, 7, 2006.
31. J. Xu and W. B. Croft. Query expansion using local and global document analysis. In SIGIR, 1996.
32. J. Xu and H. Li. AdaRank: A boosting algorithm for information retrieval. In SIGIR, 2007.
33. Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. In SIGIR, 2007.
34. C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In SIGIR, 2001.
35. Z. Zheng, H. Zha, K. Chen, and G. Sun. A regression framework for learning ranking functions using relative relevance judgements. In SIGIR, 2007.
36. D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Schökopf. Ranking on data manifolds. In NIPS, 2004.
37. X. Zhu. Semi-supervised learning literature survey. Technical Report 1530, University of Wisconsin, Madison, Computer Science Dept., 2005.