Two-layer generalization analysis for ranking using rademacher average

Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Volumn , Issue , 2010, Pages

  Chen, Wei ^a   Liu, Tie Yan ^b   Ma, Zhiming ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DOCUMENT MATCHING; GENERALISATION; GENERATION MECHANISM; I.I.D. SAMPLINGS; PERFORMANCE; QUERY DOCUMENTS; RANK ALGORITHMS; RANKING MODEL; TRAINING DATA; TWO-LAYER;

EID: 85162054015     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. S. Agarwal, T. Graepel, R. Herbrich, S.Har-Peled, and D. Roth. Generalization bounds for the area under the roc curve. Journal of Machine Learning Research, 6:393-425, 2005.
2. S. Agarwal and P. Niyogi. Generalization bounds for ranking algorithms via algorithmic stability. Journal of Machine Learning Research, 10:441-474, 2009.
3. P. L. Bartlett, S.Mendelson, andM. Long. Rademacher and gaussian complexities: risk bounds and structural results. Journal of Machine Learning Research, 3:463-482, 2002.
4. J. Baxter. Learning internal representations. In Proceedings of the Eighth International Conference on Computational Learning Theory, pages 311-320. ACM Press, 1995.
5. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In ICML '05: Proceedings of the 22nd International Conference on Machine learning, pages 89-96, 2005.
6. Y. Cao, J. Xu, T. Y. Liu, H. Li, Y. Huang, and H. W. Hon. Adapting ranking svm to document retrieval. In SIGIR '06: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 186-193. ACM Press, 2006.
7. Z. Cao, T. Qin, T. Y. Liu, M. F. Tsai, and H. Li. Learning to rank: from pairwise approach to listwise approach. In ICML '07: Proceedings of the 24th International Conference on Machine learning, pages 129-136, 2007.
8. C. L. Clarke, N. Craswell, and I. Soboroff. Overview of the trec 2009 web track. Technical report, no date.
9. S. Clémençon, G. Lugosi, and N. Vayatis. Ranking and scoring using empirical risk minimization. In COLT '05: Proceedings of the 18th Annual Conference on Learning Theory, pages 1-15, 2005.
10. D. Cossock and T. Zhang. Subset ranking using regression. In COLT '06: Proceedings of the 19th Annual Conference on Learning Theory, pages 605-619, 2006.
11. Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4:933-969, 2003.
12. R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. In Advances in Large Margin Classifiers, pages 115-132, Cambridge, MA, 1999. MIT.
13. T. Joachims. Optimizing search engines using clickthrough data. In KDD '02: Proceedings of the 8th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133-142, 2002.
14. Y. Y. Lan, T. Y. Liu, Z. M. Ma, and H. Li. Generalization analysis of listwise learning-torank algorithms. In ICML '09: Proceedings of the 26th International Conference on Machine Learning, pages 577-584, 2009.
15. Y. Y. Lan, T. Y. Liu, T. Qin, Z. M. Ma, and H. Li. Query-level stability and generalization in learning to rank. In ICML '08: Proceedings of the 25th International Conference on Machine Learning, pages 512-519, 2008.
16. T. Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3:225-331, 2009.
17. J. R.Wen, J. Y. Nie, and H. J. Zhang. Clustering user queries of a search engine. In WWW '01: Proceedings of the 10th international conference on World Wide Web, pages 162-168, New York, NY, USA, 2001. ACM.
18. F. Xia, T.-Y. Liu, J. Wang, W. Zhang, and H. Li. Listwise approach to learning to rank - theory and algorithm. In ICML '08: Proceedings of the 25th International Conference on Machine learning, pages 1192-1199. Omnipress, 2008.
19. E. Yilmaz and S. Robertson. Deep versus shallow judgments in learning to rank. In SIGIR '09: Proceedings of the 32th annual international ACM SIGIR conference on Research and development in information retrieval, pages 662-663, 2009.