-
5
-
-
31844446958
-
Learning to rank using gradient descent
-
C. J. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proc. of Internaltional Conference on Machine Learning (ICML-05), pages 89-97. 2005.
-
(2005)
Proc. of Internaltional Conference on Machine Learning (ICML-05)
, pp. 89-97
-
-
Burges, C.J.C.1
Shaked, T.2
Renshaw, E.3
Lazier, A.4
Deeds, M.5
Hamilton, N.6
Hullender, G.7
-
6
-
-
85157965754
-
Learning to rank with nonsmooth cost functions
-
MIT press, Cambridge, MA
-
C.J.C. Burges, R. Ragno, and Q. V. Le. Learning to rank with nonsmooth cost functions. In Advances in Neural Information Processing Systems (NIPS) 20. MIT press, Cambridge, MA, 2006.
-
(2006)
Advances in Neural Information Processing Systems (NIPS) 20
-
-
Burges, C.J.C.1
Ragno, R.2
Le, Q.V.3
-
8
-
-
33745616114
-
A machine learning information retrieval approach to protein fold recognition
-
J. Cheng and P. Baldi. A machine learning information retrieval approach to protein fold recognition. Bioinformatics, 22:1456-1463, 2006.
-
(2006)
Bioinformatics
, vol.22
, pp. 1456-1463
-
-
Cheng, J.1
Baldi, P.2
-
9
-
-
21844453228
-
Gaussian, processes for ordinal, regression
-
W. Chu and Z. Ghahramani. Gaussian, processes for ordinal, regression. Journal of Machine Learning Research, 6:1019-1041, 2005.
-
(2005)
Journal of Machine Learning Research
, vol.6
, pp. 1019-1041
-
-
Chu, W.1
Ghahramani, Z.2
-
12
-
-
33847626350
-
Support vector ordinal regression
-
W. Chu and S.S. Keerthi. Support vector ordinal regression. Neural Computation, 19(3), 2007.
-
(2007)
Neural Computation
, vol.19
, Issue.3
-
-
Chu, W.1
Keerthi, S.S.2
-
17
-
-
4644367942
-
An efficient boosting algorithm for combining preferences
-
Y. Freund, R. Iyer, R.E. 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.E.3
Singer, Y.4
-
18
-
-
84976668719
-
Using collaborative filtering to weave an information tapestry
-
D. Goldberg, D. Nichols, B. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35:61-70, 1992.
-
(1992)
Communications of the ACM
, vol.35
, pp. 61-70
-
-
Goldberg, D.1
Nichols, D.2
Oki, B.3
Terry, D.4
-
22
-
-
0008371352
-
Large margin rank boundaries for ordinal regression
-
A. J. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, editors, MIT Press, Cambridge, MA
-
R. Herbrich, T. Graepel, and K. Obennayer. Large margin rank boundaries for ordinal regression. In A. J. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 115-132. MIT Press, Cambridge, MA, 2000.
-
(2000)
Advances in Large Margin Classifiers
, pp. 115-132
-
-
Herbrich, R.1
Graepel, T.2
Obennayer, K.3
-
24
-
-
0242456822
-
Optimizing search engines using clickthrough data. In David Hand, Daniel
-
Keim, and Raymond NG, editors
-
I. Joachims. Optimizing search engines using clickthrough data. In David Hand, Daniel. Keim, and Raymond NG, editors, Proc. of 8th ACM SIGKDD International conference on knowledge discovery and data mining, pages 133-142. 2002.
-
(2002)
Proc. of 8th ACM SIGKDD International conference on knowledge discovery and data mining
, pp. 133-142
-
-
Joachims, I.1
-
25
-
-
0035401786
-
Prediction of ordinal classes using regression trees
-
S. Kramer, G. Widmer, B. Pfahringer, and M. DeGroeve. Prediction of ordinal classes using regression trees. Fundamenta Informaticae, 47:1-13, 2001.
-
(2001)
Fundamenta Informaticae
, vol.47
, pp. 1-13
-
-
Kramer, S.1
Widmer, G.2
Pfahringer, B.3
DeGroeve, M.4
-
27
-
-
0002704818
-
A practical bayesian framework for back propagation. networks
-
D. J. C. MacKay. A practical bayesian framework for back propagation. networks. Neural Computation, 4:448-472, 1992.
-
(1992)
Neural Computation
, vol.4
, pp. 448-472
-
-
MacKay, D.J.C.1
-
31
-
-
0028961335
-
SCOP: A. structural classification of proteins database for the investigation of sequences and structures
-
A. G. Murzin, S. E. Brenner, T. Hubbard, and C. Chothia. SCOP: A. structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol., 247:536-540, 1995.
-
(1995)
J. Mol. Biol
, vol.247
, pp. 536-540
-
-
Murzin, A.G.1
Brenner, S.E.2
Hubbard, T.3
Chothia, C.4
-
33
-
-
9444298517
-
-
S. Rajaram, A. Garg, X.S. Zhou, and T.S. Huang. Classification approach towards ranking and sorting problems. In Machine Learning: ECML 2003, 2837 of Lecture Notes in Artificail Intelligence (N. Lavrac, D. gamberger, H. Blocked and L Todorovski eds.), pages 301-312. Springer-Verlag, 2003.
-
S. Rajaram, A. Garg, X.S. Zhou, and T.S. Huang. Classification approach towards ranking and sorting problems. In Machine Learning: ECML 2003, vol. 2837 of Lecture Notes in Artificail Intelligence (N. Lavrac, D. gamberger, H. Blocked and L Todorovski eds.), pages 301-312. Springer-Verlag, 2003.
-
-
-
-
34
-
-
0001595997
-
Neural, network classifiers estimate bayesian a-posteriori probabilities
-
M.D. Richard and R.P. Lippman. Neural, network classifiers estimate bayesian a-posteriori probabilities. Neural Computation, 3:461-483, 1991.
-
(1991)
Neural Computation
, vol.3
, pp. 461-483
-
-
Richard, M.D.1
Lippman, R.P.2
-
35
-
-
56349169314
-
-
D.E. Rumelhart, G.E. Hinton, and R.J. Williams. Learning Internal Representations by Error Propagation. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. I: Foundations, pages 318-362. Bradford Books/MIT Press, Cambridge, MA., 1986.
-
D.E. Rumelhart, G.E. Hinton, and R.J. Williams. Learning Internal Representations by Error Propagation. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. I: Foundations, pages 318-362. Bradford Books/MIT Press, Cambridge, MA., 1986.
-
-
-
-
36
-
-
0003408420
-
-
MIT University Press, Cambridge, MA
-
B. Schölkopf and A.J. Smola. Learning with Kernels, Support Vector Machines, Regularization, Optimization and Beyond. MIT University Press, Cambridge, MA, 2002.
-
(2002)
Learning with Kernels, Support Vector Machines, Regularization, Optimization and Beyond
-
-
Schölkopf, B.1
Smola, A.J.2
|