Learning bilinear model for matching queries and documents
暂无分享,去创建一个
Wei Wu | Hang Li | Zhengdong Lu | Zhengdong Lu | Hang Li | Wei Wu
[1] Ricardo A. Baeza-Yates,et al. Extracting semantic relations from query logs , 2007, KDD '07.
[2] Hang Li. Learning to Rank for Information Retrieval and Natural Language Processing , 2011, Synthesis Lectures on Human Language Technologies.
[3] Yong Yu,et al. SVDFeature: a toolkit for feature-based collaborative filtering , 2012, J. Mach. Learn. Res..
[4] Jacob A. Wegelin,et al. A Survey of Partial Least Squares (PLS) Methods, with Emphasis on the Two-Block Case , 2000 .
[5] Wei Wu,et al. Learning a Robust Relevance Model for Search Using Kernel Methods , 2011, J. Mach. Learn. Res..
[6] Tie-Yan Liu,et al. Adapting ranking SVM to document retrieval , 2006, SIGIR.
[7] Manik Varma,et al. More generality in efficient multiple kernel learning , 2009, ICML '09.
[8] Thorsten Joachims,et al. Optimizing search engines using clickthrough data , 2002, KDD.
[9] Tomer Hertz,et al. Boosting margin based distance functions for clustering , 2004, ICML.
[10] Michael McGill,et al. Introduction to Modern Information Retrieval , 1983 .
[11] Koby Crammer,et al. Pranking with Ranking , 2001, NIPS.
[12] Yanjun Qi,et al. Supervised semantic indexing , 2009, ECIR.
[13] Kaizhu Huang,et al. Sparse Metric Learning via Smooth Optimization , 2009, NIPS.
[14] Alexander J. Smola,et al. Learning the Kernel with Hyperkernels , 2005, J. Mach. Learn. Res..
[15] Hang Li,et al. AdaRank: a boosting algorithm for information retrieval , 2007, SIGIR.
[16] Quoc V. Le,et al. Abstract , 2003, Appetite.
[17] Jaana Kekäläinen,et al. IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.
[18] CHENGXIANG ZHAI,et al. A study of smoothing methods for language models applied to information retrieval , 2004, TOIS.
[19] Michael I. Jordan,et al. Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.
[20] Cynthia Rudin,et al. Margin-Based Ranking Meets Boosting in the Middle , 2005, COLT.
[21] Charles A. Micchelli,et al. Learning the Kernel Function via Regularization , 2005, J. Mach. Learn. Res..
[22] Peter L. Bartlett,et al. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , 2003, J. Mach. Learn. Res..
[23] Francis R. Bach,et al. A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization , 2008, J. Mach. Learn. Res..
[24] Stephen E. Robertson,et al. Okapi at TREC , 1992, TREC.
[25] Stephen E. Robertson,et al. Okapi at TREC-3 , 1994, TREC.
[26] Michael R. Lyu,et al. Learning latent semantic relations from clickthrough data for query suggestion , 2008, CIKM '08.
[27] Nick Craswell,et al. Random walks on the click graph , 2007, SIGIR.
[28] Thorsten Joachims,et al. Learning a Distance Metric from Relative Comparisons , 2003, NIPS.
[29] Jianfeng Gao,et al. Clickthrough-based latent semantic models for web search , 2011, SIGIR.
[30] Michael I. Jordan,et al. Multiple kernel learning, conic duality, and the SMO algorithm , 2004, ICML.
[31] Yi Liu,et al. An Efficient Algorithm for Local Distance Metric Learning , 2006, AAAI.
[32] Nello Cristianini,et al. Learning the Kernel Matrix with Semidefinite Programming , 2002, J. Mach. Learn. Res..
[33] Tie-Yan Liu,et al. Two-Layer Generalization Analysis for Ranking Using Rademacher Average , 2010, NIPS.
[34] Thore Graepel,et al. Large Margin Rank Boundaries for Ordinal Regression , 2000 .
[35] Inderjit S. Dhillon,et al. Information-theoretic metric learning , 2006, ICML '07.
[36] Roman Rosipal,et al. Overview and Recent Advances in Partial Least Squares , 2005, SLSFS.
[37] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[38] Samy Bengio,et al. A Discriminative Kernel-Based Approach to Rank Images from Text Queries , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[39] Francis R. Bach,et al. Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning , 2008, NIPS.
[40] Tie-Yan Liu,et al. Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.
[41] Tommi S. Jaakkola,et al. Maximum-Margin Matrix Factorization , 2004, NIPS.
[42] Kilian Q. Weinberger,et al. Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.
[43] Shivani Agarwal,et al. Stability and Generalization of Bipartite Ranking Algorithms , 2005, COLT.
[44] Corinna Cortes,et al. Invited talk: Can learning kernels help performance? , 2009, International Conference on Machine Learning.
[45] Peter J. Schreier,et al. A Unifying Discussion of Correlation Analysis for Complex Random Vectors , 2008, IEEE Transactions on Signal Processing.
[46] Thomas Hofmann,et al. Latent semantic models for collaborative filtering , 2004, TOIS.
[47] John Shawe-Taylor,et al. Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.
[48] Tie-Yan Liu,et al. Learning to rank for information retrieval , 2009, SIGIR.
[49] Masashi Sugiyama,et al. Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis , 2007, J. Mach. Learn. Res..
[50] N. Cristianini,et al. On Kernel-Target Alignment , 2001, NIPS.
[51] Richard A. Harshman,et al. Indexing by Latent Semantic Analysis , 1990, J. Am. Soc. Inf. Sci..
[52] Wei Liu,et al. Learning Distance Metrics with Contextual Constraints for Image Retrieval , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[53] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[54] David Grangier,et al. A Discriminative Kernel-based Model to Rank Images from Text Queries , 2007 .
[55] Hang Li,et al. Relevance Ranking Using Kernels , 2010, AIRS.