Adapting boosting for information retrieval measures
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Bacchiani, M., Roark, B., & Saraclar, M. (2004). Language model adaptation with MAP estimation and the perceptron algorithm. In HLT-NAACL (pp. 21–24).
Bellagarda, J. (2001). An overview of statistical language model adaptation. In ITRW on adaptation methods for speech recognition (pp. 165–174).
Burges, C. (2005). Ranking as learning structured outputs. In C. C. S. Agarwal & R. Herbrich (Eds.), Proceedings of the NIPS workshop on learning to rank.
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., et al. (2005). Learning to rank using gradient descent. In ICML. Bonn, Germany.
Cao, Z., Qin, T., Liu, T. Y., Tsai, M. F., & Li, H. (2007). Learning to rank: From pairwise approach to listwise approach. In ICML.
Chen, K., Lu, R., Wong, C., Sun, G., Heck, L., & Tseng, B. (2008). Trada: Tree based ranking function adaptation. In ACM 17th conference on information and knowledge management.
Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5).
Gao, J., Nie, J. Y., Wu, G., & Cao, G. (2004). Dependence language model for information retrieval. In SIGIR, (pp. 170–177).
Gao, J., Qin, H., Xia, X., & Nie, J. Y. (2005). Linear discriminative model for information retrieval. In SIGIR, (pp. 290–297).
Gao, J., Suzuki, H., & Yuan, W. (2006). An empirical study on language model adaptation. ACM Trans on Asian Language Information Processing, 5(3), 207–227.
Gao, J., Wu, Q., Burges, C., Svore, K., Su, Y., Khan, N., et al. (2009). Model adaptation via model interpolation and boosting for web search ranking. In Conference on Empirical Methods in Natural Language Processing.
Jarvelin, K., & Kekalainen, J. (2000). IR evaluation methods for retrieving highly relevant documents. In SIGIR 23. ACM.
Jones, K., Walker, S., & Robertson, S. (1998). A probabilistic model of information retrieval: Development and status. Tech. Rep. TR-446, Cambridge University Computer Laboratory.
Le, Q., & Smola, A. J. (2007). Direct optimization of ranking measures. CoRR abs/0704.3359. Informal publication.
Li, P., Burges, C., & Wu, Q. (2007). Learning to rank using classification and gradient boosting. In NIPS.
Mason, L., Baxter, J., Bartlett, P., & Frean, M. (2000). Boosting algorithms as gradient descent. In T. L. S. A. Solla & K. R. Müller (Eds.), Advances in neural information processing systems (Vol. 12, pp. 512–518).
Robertson, S., & Zaragoza, H. (2007). On rank-based effectiveness measures and optimization. Information Retrieval, 10(3), 321–339.
Song, F., & Croft, B. (1999). A general language model for information retrieval. In CIKM (pp. 316–321).
Yue, Y., & Burges, C. (2007). On using simultaneous perturbation stochastic approximation for learning to rank, and the empirical optimality of LambdaRank. Tech. Rep. MSR-TR-2007-115, Microsoft research.
Yue, Y., Finley, T., Radlinski, F., & Joachims, T. (2007). A support vector method for optimizing average precision. In SIGIR.
Zhai, C., & Lafferty, J. (2002). Two-stage language models for information retrieval. In SIGIR (pp. 49–56).
Zheng, Z., Zha, H., Zhang, T., Chapelle, O., Chen, K., & Sun, G. (2007). A general boosting method and its application to learning ranking functions for web search. In NIPS.