-
1
-
-
83055196829
-
Million query track 2008 overview
-
National Institute of Standards and Technology
-
J. Allan, B. Carterette, J. A. Aslam, V. Pavlu, and E. Kanoulas. Million query track 2008 overview. In The Sixteenth Text REtrieval Conference Proceedings (TREC 2008). National Institute of Standards and Technology, 2009.
-
(2009)
The Sixteenth Text REtrieval Conference Proceedings (TREC 2008)
-
-
Allan, J.1
Carterette, B.2
Aslam, J.A.3
Pavlu, V.4
Kanoulas, E.5
-
5
-
-
33750729556
-
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
-
M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 2006.
-
(2006)
Journal of Machine Learning Research
-
-
Belkin, M.1
Niyogi, P.2
Sindhwani, V.3
-
6
-
-
31844446958
-
Learning to rank using gradient descent
-
C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Intl. Conf. on Machine Learning (ICML), 2005.
-
Intl. Conf. on Machine Learning (ICML), 2005
-
-
Burges, C.1
Shaked, T.2
Renshaw, E.3
Lazier, A.4
Deeds, M.5
Hamilton, N.6
Hullender, G.7
-
10
-
-
79953238334
-
Semi-supervised ranking aggregation
-
May
-
S. Chen, F. Wang, Y. Song, and C. Zhang. Semi-supervised ranking aggregation. Information Processing & Management, 47:415-425, May 2011.
-
(2011)
Information Processing & Management
, vol.47
, pp. 415-425
-
-
Chen, S.1
Wang, F.2
Song, Y.3
Zhang, C.4
-
11
-
-
36849036983
-
Extensions of gaussian processes for ranking: Semi-supervised and active learning
-
W. Chu and Z. Ghahramani. Extensions of gaussian processes for ranking: Semi-supervised and active learning. In NIPS workshop on Learning to Rank, 2005.
-
NIPS Workshop on Learning to Rank, 2005
-
-
Chu, W.1
Ghahramani, Z.2
-
12
-
-
35548956873
-
Regularizing query-based retrieval scores
-
F. Diaz. Regularizing query-based retrieval scores. Information Retrieval, 10(6):531-562, 2007.
-
(2007)
Information Retrieval
, vol.10
, Issue.6
, pp. 531-562
-
-
Diaz, F.1
-
15
-
-
4644367942
-
An efficient boosting algorithm for combining preferences
-
Y. Freund, R. Iyer, R. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4:933-969, 2003.
-
(2003)
Journal of Machine Learning Research
, vol.4
, pp. 933-969
-
-
Freund, Y.1
Iyer, R.2
Schapire, R.3
Singer, Y.4
-
18
-
-
0001938951
-
Transductive inference for text classification using support vector machines
-
T. Joachims. Transductive inference for text classification using support vector machines. In Intl. Conf. on Machine Learning (ICML), pages 200-209, 1999.
-
(1999)
Intl. Conf. on Machine Learning (ICML)
, pp. 200-209
-
-
Joachims, T.1
-
20
-
-
0012435995
-
A probabilistic model of information retrieval: Development and comparative experiments
-
K. S. Jones, S. Walker, and S. E. Robertson. A probabilistic model of information retrieval: development and comparative experiments. In Information Processing & Management, pages 779-840, 2000.
-
(2000)
Information Processing & Management
, pp. 779-840
-
-
Jones, K.S.1
Walker, S.2
Robertson, S.E.3
-
21
-
-
64549161526
-
Semi-supervised document retrieval
-
M. Li, H. Li, and Z.-H. Zhou. Semi-supervised document retrieval. Information Processing & Management, 45(3):341-355, 2009.
-
(2009)
Information Processing & Management
, vol.45
, Issue.3
, pp. 341-355
-
-
Li, M.1
Li, H.2
Zhou, Z.-H.3
-
23
-
-
77954568972
-
LETOR: A benchmark collection for research on learning to rank for information retrieval
-
T. Qin, T.-Y. Liu, J. Xu, and H. Li. LETOR: A benchmark collection for research on learning to rank for information retrieval. Information Retrieval Journal, 2010.
-
(2010)
Information Retrieval Journal
-
-
Qin, T.1
Liu, T.-Y.2
Xu, J.3
Li, H.4
-
25
-
-
79952401779
-
LambdaMerge: Merging the results of query reformulations
-
D. Sheldon, M. Shokouhi, M. Szummer, and N. Craswell. LambdaMerge: Merging the results of query reformulations. In Conf. Web search and data mining (WSDM), pages 795-804, 2011.
-
(2011)
Conf. Web Search and Data Mining (WSDM)
, pp. 795-804
-
-
Sheldon, D.1
Shokouhi, M.2
Szummer, M.3
Craswell, N.4
-
29
-
-
83055165625
-
Cost-sensitive machine learning for information retrieval
-
Krishnapuram, Yu, and Rao, editors
-
M. Szummer and F. Radlinski. Cost-sensitive machine learning for information retrieval. In Krishnapuram, Yu, and Rao, editors, Cost-sensitive Machine Learning. Chapman and Hall/CRC, 2011.
-
(2011)
Cost-sensitive Machine Learning. Chapman and Hall/CRC
-
-
Szummer, M.1
Radlinski, F.2
-
33
-
-
38049178360
-
Supervised and semi-supervised machine learning ranking
-
Comparative Evaluation of XML Information Retrieval Systems, Springer
-
J.-N. Vittaut and P. Gallinari. Supervised and semi-supervised machine learning ranking. In Comparative Evaluation of XML Information Retrieval Systems, volume 4518 of Lecture Notes in Computer Science, pages 213-222. Springer, 2007.
-
(2007)
Lecture Notes in Computer Science
, vol.4518
, pp. 213-222
-
-
Vittaut, J.-N.1
Gallinari, P.2
|