Information Technology Reference
In-Depth Information
16.2 As mentioned in this chapter, the existing listwise ranking algorithms cannot
fit into the two-layer ranking framework. Show how to modify these algo-
rithms in order to leverage the two-layer ranking framework to analyze their
theoretical properties.
References
1. Agarwal, S.: Generalization bounds for some ordinal regression algorithms. In: Proceedings
of the 19th International Conference on Algorithmic Learning Theory (ALT 2008), pp. 7-21
(2008)
2. Agarwal, S., Graepel, T., Herbrich, R., Har-Peled, S., Roth, D.: Generalization bounds for the
area under the roc curve. Journal of Machine Learning 6 , 393-425 (2005)
3. Agarwal, S., Niyogi, P.: Stability and generalization of bipartite ranking algorithms. In: Pro-
ceedings of the 18th Annual Conference on Learning Theory (COLT 2005), pp. 32-47 (2005)
4. Chen, W., Liu, T.Y., Ma, Z.M.: Two-layer generalization analysis for ranking using
rademacher average. In: Lafferty, J., Williams, C.K.I., Shawe-Taylor, J., Zemel, R., Culotta,
A. (eds.) Advances in Neural Information Processing Systems 23 (NIPS 2010), pp. 370-378
(2011)
5. Clemencon, S., Lugosi, G., Vayatis, N.: Ranking and empirical minimization of U-statistics.
The Annals of Statistics 36 (2), 844-874 (2008)
6. Cossock, D., Zhang, T.: Subset ranking using regression. In: Proceedings of the 19th Annual
Conference on Learning Theory (COLT 2006), pp. 605-619 (2006)
7. Freund, Y., Iyer, R., Schapire, R., Singer, Y.: An efficient boosting algorithm for combining
preferences. Journal of Machine Learning Research 4 , 933-969 (2003)
8. Lan, Y., Liu, T.Y.: Generalization analysis of listwise learning-to-rank algorithms. In: Proceed-
ings of the 26th International Conference on Machine Learning (ICML 2009), pp. 577-584
(2009)
9. Lan, Y., Liu, T.Y., Qin, T., Ma, Z., Li, H.: Query-level stability and generalization in learning
to rank. In: Proceedings of the 25th International Conference on Machine Learning (ICML
2008), pp. 512-519 (2008)
10. Rajaram, S., Agarwal, S.: Generalization bounds for k-partite ranking. In: NIPS 2005 Work-
shop on Learning to Rank (2005)
Search WWH ::




Custom Search