[1]Chapelle O, Keerthi S S. Efficient algorithms for ranking with SVMs[J]. Information Retrieval, 2010,13(3):201-215.
[2]Burges C, Shaked T, Renshaw E, et al. Learning to rank using gradient descent[C]// International Conference on Machine Learning. 2005:89-96.
[3]Zheng Zhaohui, Zha Hongyuan, Zhang Tong, et al. A general Boosting method and its application to learning ranking functions for Web search[C]// Proceedings of the 2007
Conference on Advances in Neural Information Processing Systems. 2007:1697-1704.
[4]黄震华,张佳雯,田春岐,等. 基于排序学习的推荐算法研究综述[J]. 软件学报, 2016,27(3):691-713.
[5]Cao Houwei, Verma R, Nenkova A. Speakersensitive emotion recognition via ranking: Studies on acted and spontaneous speech[J]. Computer Speech & Language, 2015,28
(1):186-202.
[6]Song Yang, Wang Hongning, He Xiaodong. Adapting deep RankNet for personalized search[C]// ACM International Conference on Web Search and Data Mining. 2014,144(5):S-471.
[7]Miao Zhigao, Wang Juan, Zhou Aimin, et al. Regularized boost for semisupervised ranking[C]// Proceedings of the 18th Asia Pacific Symposium on Intelligent and
Evolutionary Systems. Springer International Publishing, 2015:643-651.
[8]Li Hang. Learning to rank for information retrieval and natural language processing[M]// Synthesis Lectures on Human Language Technologies. Morgan & Claypool, 2011:113.
[9]Panda B, Herbach J S, Basu S, et al. PLANET: Massively parallel learning of tree ensembles with MapReduce[J]. Proceedings of the Vldb Endowment, 2009,2(2):1426-1437.
[10]Ge Guangtao, Wong G W. Classification of premalignant pancreatic cancer massspectrometry data using decision tree ensembles[J]. BMC Bioinformatics, 2008,9(1):275.
[11]Schietgat L, Vens C, Struyf J, et al. Predicting gene function using hierarchical multilabel decision tree ensembles[J]. BMC Bioinformatics, 2010,11(1):1-14.
[12]Criminisi A, Shotton J, Konukoglu E. Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and SemiSupervised
Learning[R]. Microsoft Technical Report, MSR-TR-2011-114, 2011.
[13]Mohan A, Chen Z, Weinberger K Q. Websearch ranking with initialized gradient boosted regression trees[C]// Jmlr: Workshop & Conference. 2011:77-89.
[14]Breiman L. Random forests[J]. Machine Learning, 2001,45(1):5-32.
[15]Burges C J C , Svore K M, Bennett P N, et al. Learning to rank using an ensemble of LambdaGradient models[J]. Journal of Machine Learning Research, 2011,14:25-35.
[16]Burges C J C. From Ranknet to Lambdarank to Lambdamart: An Overview[R]. MSR-TR-2010-82, 2010.
[17]Donmez P, Svore K M, Burges C J C. On the local optimality of LambdaRank[C]// International ACM SIGIR Conference on Research and Development in Information Retrieval,
SIGIR 2009. 2009:460-467.
[18]Ganjisaffar Y, Caruana R, Lopes C V. Bagging gradientboosted trees for high precision, low variance ranking models[C]// Proceeding of the, International ACM SIGIR
Conference on Research and Development in Information Retrieval, SIGIR 2011. 2011:85-94.
[19]Chapelle O, Metlzer D, Zhang Ya, et al. Expected reciprocal rank for graded relevance[C]// ACM Conference on Information and Knowledge Management, CIKM 2009. 2009:621
-630.
[20]Wang Yining, Wang Liwei, Li Yuanzhi, et al. A theoretical analysis of NDCG type ranking measures[J]. Journal of Machine Learning Research, 2013,30:25-54.
[21]Qin Tao, Liu Tieyan, Xu Jun, et al. LETOR: A benchmark collection for research on learning to rank for information retrieval[J]. Information Retrieval, 2010,13
(4):346-374. |