-
1
-
-
84898946229
-
Support vector machines for multiple-instance learning
-
S. Becker, S. Thrun, and K. Obermayer, editors, MIT Press, Cambridge, MA
-
S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 577-584. MIT Press, Cambridge, MA, 2003.
-
(2003)
Advances in Neural Information Processing Systems
, vol.15
, pp. 577-584
-
-
Andrews, S.1
Tsochantaridis, I.2
Hofmann, T.3
-
2
-
-
0000492326
-
Learning from noisy examples
-
D. Angluin and P. Laird. Learning from noisy examples. Machine Learning, 2(4):343-370, 1988.
-
(1988)
Machine Learning
, vol.2
, Issue.4
, pp. 343-370
-
-
Angluin, D.1
Laird, P.2
-
3
-
-
14344252374
-
Multiple kernel learning, conic duality, and the SMO algorithm
-
Banff, Canada
-
F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In Proceedings of the 21st International Conference on Machine Learning, pages 41-48, Banff, Canada, 2004.
-
(2004)
Proceedings of the 21st International Conference on Machine Learning
, pp. 41-48
-
-
Bach, F.R.1
Lanckriet, G.R.G.2
Jordan, M.I.3
-
4
-
-
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, 7:2399-2434, 2006.
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 2399-2434
-
-
Belkin, M.1
Niyogi, P.2
Sindhwani, V.3
-
6
-
-
80052884721
-
Multi-label learning with incomplete class assignments
-
Colorado Springs, CO
-
S. S. Bucak, R. Jin, and A. K. Jain. Multi-label learning with incomplete class assignments. In Proceedings of International Conference on Computer Vision and Pattern Recognition, pages 2801-2808, Colorado Springs, CO, 2011.
-
(2011)
Proceedings of International Conference on Computer Vision and Pattern Recognition
, pp. 2801-2808
-
-
Bucak, S.S.1
Jin, R.2
Jain, A.K.3
-
7
-
-
34247849152
-
Training a support vector machine in the primal
-
O. Chapelle. Training a support vector machine in the primal. Neural Computation, 19(5):1155-1178, 2007.
-
(2007)
Neural Computation
, vol.19
, Issue.5
, pp. 1155-1178
-
-
Chapelle, O.1
-
9
-
-
33749257143
-
A continuation method for semi-supervised SVMs
-
Pittsburgh, PA
-
O. Chapelle, M. Chi, and A. Zien. A continuation method for semi-supervised SVMs. In Proceedings of the 23rd International Conference on Machine Learning, pages 185-192, Pittsburgh, PA, 2006a.
-
(2006)
Proceedings of the 23rd International Conference on Machine Learning
, pp. 185-192
-
-
Chapelle, O.1
Chi, M.2
Zien, A.3
-
10
-
-
33749252873
-
-
MIT Press, Cambridge, MA, USA
-
O. Chapelle, B. Schölkopf, and A. Zien. Semi-Supervised Learning. MIT Press, Cambridge, MA, USA, 2006b.
-
(2006)
Semi-supervised Learning
-
-
Chapelle, O.1
Schölkopf, B.2
Zien, A.3
-
11
-
-
84864069202
-
Branch and bound for semi-supervised support vector machines
-
B. Schölkopf, J. Platt, and T. Hoffman, editors, MIT Press, Cambridge, MA
-
O. Chapelle, V. Sindhwani, and S. S. Keerthi. Branch and bound for semi-supervised support vector machines. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 217-224. MIT Press, Cambridge, MA, 2007.
-
(2007)
Advances in Neural Information Processing Systems 19
, pp. 217-224
-
-
Chapelle, O.1
Sindhwani, V.2
Keerthi, S.S.3
-
13
-
-
33749243963
-
A regularization framework for multiple-instance learning
-
Pittsburgh, PA, USA
-
P. M. Cheung and J. T. Kwok. A regularization framework for multiple-instance learning. In Proceedings of the 23th International Conference on Machine Learning, pages 193-200, Pittsburgh, PA, USA, 2006.
-
(2006)
Proceedings of the 23th International Conference on Machine Learning
, pp. 193-200
-
-
Cheung, P.M.1
Kwok, J.T.2
-
14
-
-
33747128180
-
Large scale transductive SVMs
-
R. Collobert, F. Sinz, J. Weston, and L. Bottou. Large scale transductive SVMs. Journal of Machine Learning Research, 7:1687-1712, 2006.
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 1687-1712
-
-
Collobert, R.1
Sinz, F.2
Weston, J.3
Bottou, L.4
-
15
-
-
0010442827
-
On the algorithmic implementation of multiclass kernel-based vector machines
-
K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research, 2:265-292, 2002.
-
(2002)
Journal of Machine Learning Research
, vol.2
, pp. 265-292
-
-
Crammer, K.1
Singer, Y.2
-
16
-
-
84898936871
-
On kernel-target alignment
-
T. G. Dietterich, Z. Becker, and Z. Ghahramani, editors, MIT Press, Cambridge, MA
-
N. Cristianini, J. Shawe-Taylor, A. Elisseeff, and J. Kandola. On kernel-target alignment. In T. G. Dietterich, Z. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 367-373. MIT Press, Cambridge, MA, 2002.
-
(2002)
Advances in Neural Information Processing Systems
, vol.14
, pp. 367-373
-
-
Cristianini, N.1
Shawe-Taylor, J.2
Elisseeff, A.3
Kandola, J.4
-
17
-
-
67049145116
-
Semi-supervised learning using semi-definite programming
-
O. Chapelle, B. Schölkopf, and A. Zien, editors, MIT Press, Cambridge, MA
-
T. De Bie and N. Cristianini. Semi-supervised learning using semi-definite programming. In O. Chapelle, B. Schölkopf, and A. Zien, editors, Semi-Supervised Learning. MIT Press, Cambridge, MA, 2006.
-
(2006)
Semi-supervised Learning
-
-
De Bie, T.1
Cristianini, N.2
-
18
-
-
29644438050
-
Statistical comparisons of classifiers over multiple data sets
-
J. Demsar. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7:1-30, 2006.
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 1-30
-
-
Demsar, J.1
-
19
-
-
0030649484
-
Solving the multiple instance problem with axis-parallel rectangles
-
T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89 (1-2):31-71, 1997.
-
(1997)
Artificial Intelligence
, vol.89
, Issue.1-2
, pp. 31-71
-
-
Dietterich, T.G.1
Lathrop, R.H.2
Lozano-Pérez, T.3
-
20
-
-
29144499905
-
Working set selection using second order information for training support vector machines
-
R. E. Fan, P. H. Chen, and C. J. Lin. Working set selection using second order information for training support vector machines. Journal of Machine Learning Research, 6:1889-1918, 2005.
-
(2005)
Journal of Machine Learning Research
, vol.6
, pp. 1889-1918
-
-
Fan, R.E.1
Chen, P.H.2
Lin, C.J.3
-
21
-
-
4444252785
-
Multi-instance kernels
-
Sydney, Australia
-
T. Gärtner, P. A. Flach, A. Kowalczyk, and A. J. Smola. Multi-instance kernels. In Proceedings of the 19th International Conference on Machine Learning, pages 179-186, Sydney, Australia, 2002.
-
(2002)
Proceedings of the 19th International Conference on Machine Learning
, pp. 179-186
-
-
Gärtner, T.1
Flach, P.A.2
Kowalczyk, A.3
Smola, A.J.4
-
22
-
-
70549090136
-
Max-margin multiple-instance learning via semidefinite programming
-
Nanjing, China
-
Y. Guo. Max-margin multiple-instance learning via semidefinite programming. In Proceedings of the 1st Asian Conference on Machine Learning, pages 98-108, Nanjing, China, 2009.
-
(2009)
Proceedings of the 1st Asian Conference on Machine Learning
, pp. 98-108
-
-
Guo, Y.1
-
24
-
-
56449086680
-
A dual coordinate descent method for large-scale linear SVM
-
Helsinki, Finland
-
C. J. Hsieh, K. W. Chang, C. J. Lin, S. S. Keerthi, and S. Sundararajan. A dual coordinate descent method for large-scale linear SVM. In Proceedings of the 25th International Conference on Machine Learning, pages 408-415, Helsinki, Finland, 2008.
-
(2008)
Proceedings of the 25th International Conference on Machine Learning
, pp. 408-415
-
-
Hsieh, C.J.1
Chang, K.W.2
Lin, C.J.3
Keerthi, S.S.4
Sundararajan, S.5
-
26
-
-
0001938951
-
Transductive inference for text classification using support vector machines
-
Bled, Slovenia
-
T. Joachims. Transductive inference for text classification using support vector machines. In Proceedings of the 16th International Conference on Machine Learning, pages 200-209, Bled, Slovenia, 1999.
-
(1999)
Proceedings of the 16th International Conference on Machine Learning
, pp. 200-209
-
-
Joachims, T.1
-
29
-
-
69649090538
-
A minimax theorem with applications to machine learning, signal processing, and finance
-
S.-J. Kim and S. Boyd. A minimax theorem with applications to machine learning, signal processing, and finance. SIAM Journal on Optimization, 19(3):1344-1367, 2008.
-
(2008)
SIAM Journal on Optimization
, vol.19
, Issue.3
, pp. 1344-1367
-
-
Kim, S.-J.1
Boyd, S.2
-
30
-
-
84858738634
-
Efficient and accurate lp-norm multiple kernel learning
-
Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, MIT Press, Cambridge, MA
-
M. Kloft, U. Brefeld, S. Sonnenburg, P. Laskov, K.-R. Müller, and A. Zien. Efficient and accurate lp-norm multiple kernel learning. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 997-1005. MIT Press, Cambridge, MA, 2009.
-
(2009)
Advances in Neural Information Processing Systems
, vol.22
, pp. 997-1005
-
-
Kloft, M.1
Brefeld, U.2
Sonnenburg, S.3
Laskov, P.4
Müller, K.-R.5
Zien, A.6
-
31
-
-
8844278523
-
Learning the kernel matrix with semidefinite programming
-
G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. I. Jordan. Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, 5:27-72, 2004.
-
(2004)
Journal of Machine Learning Research
, vol.5
, pp. 27-72
-
-
Lanckriet, G.R.G.1
Cristianini, N.2
Bartlett, P.3
El Ghaoui, L.4
Jordan, M.I.5
-
33
-
-
70349967917
-
A convex method for locating regions of interest with multi-instance learning
-
Bled, Slovenia
-
Y.-F. Li, J. T. Kwok, I. W. Tsang, and Z.-H. Zhou. A convex method for locating regions of interest with multi-instance learning. In Proceedings of the 20th European Conference on Machine Learning and Knowledge Discovery in Databases, pages 15-30, Bled, Slovenia, 2009a.
-
(2009)
Proceedings of the 20th European Conference on Machine Learning and Knowledge Discovery in Databases
, pp. 15-30
-
-
Li, Y.-F.1
Kwok, J.T.2
Tsang, I.W.3
Zhou, Z.-H.4
-
34
-
-
71149093081
-
Semi-supervised learning using label mean
-
Montreal, Canada
-
Y.-F. Li, J. T. Kwok, and Z.-H. Zhou. Semi-supervised learning using label mean. In Proceedings of the 26th International Conference on Machine Learning, pages 633-640, Montreal, Canada, 2009b.
-
(2009)
Proceedings of the 26th International Conference on Machine Learning
, pp. 633-640
-
-
Li, Y.-F.1
Kwok, J.T.2
Zhou, Z.-H.3
-
35
-
-
84862291617
-
Tighter and convex maximum margin clustering
-
Clearwater Beach, FL
-
Y.-F. Li, I. W. Tsang, J. T. Kwok, and Z.-H. Zhou. Tighter and convex maximum margin clustering. In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, pages 344-351, Clearwater Beach, FL, 2009c.
-
(2009)
Proceedings of the 12th International Conference on Artificial Intelligence and Statistics
, pp. 344-351
-
-
Li, Y.-F.1
Tsang, I.W.2
Kwok, J.T.3
Zhou, Z.-H.4
-
36
-
-
84868268151
-
Towards discovering what patterns trigger what labels
-
Toronto, Canada
-
Y.-F. Li, J.-H. Hu, Y. Jiang, and Z.-H. Zhou. Towards discovering what patterns trigger what labels. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, pages 1012-1018, Toronto, Canada, 2012.
-
(2012)
Proceedings of the 26th AAAI Conference on Artificial Intelligence
, pp. 1012-1018
-
-
Li, Y.-F.1
Hu, J.-H.2
Jiang, Y.3
Zhou, Z.-H.4
-
37
-
-
0041940559
-
Applications of second-order cone programming
-
M. S. Lobo, L. Vandenberghe, S. Boyd, and H. Lebret. Applications of second-order cone programming. Linear algebra and its applications, 284(1):193-228, 1998.
-
(1998)
Linear Algebra and its Applications
, vol.284
, Issue.1
, pp. 193-228
-
-
Lobo, M.S.1
Vandenberghe, L.2
Boyd, S.3
Lebret, H.4
-
39
-
-
34548084959
-
-
Technical report, Machine Learning Department, Carnegie Mellon University
-
T. M. Mitchell. The discipline of machine learning. Technical report, Machine Learning Department, Carnegie Mellon University, 2006.
-
(2006)
The Discipline of Machine Learning
-
-
Mitchell, T.M.1
-
41
-
-
0003120218
-
Fast training of support vector machines using sequential minimal optimization
-
MIT Press, Cambridge, MA, USA
-
J. C. Platt. Fast training of support vector machines using sequential minimal optimization. In Advances in Kernel Methods - Support Vector Learning, pages 185-208. MIT Press, Cambridge, MA, USA, 1999.
-
(1999)
Advances in Kernel Methods - Support Vector Learning
, pp. 185-208
-
-
Platt, J.C.1
-
42
-
-
57249084590
-
Simple MKL
-
A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet. SimpleMKL. Journal of Machine Learning Research, 9:2491-2521, 2008.
-
(2008)
Journal of Machine Learning Research
, vol.9
, pp. 2491-2521
-
-
Rakotomamonjy, A.1
Bach, F.R.2
Canu, S.3
Grandvalet, Y.4
-
44
-
-
34547964973
-
Pegasos: Primal estimated sub-gradient solver for svm
-
Corvallis, OR
-
S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for svm. In Proceedings of the 24th International Conference on Machine Learning, pages 807-814, Corvallis, OR, 2007.
-
(2007)
Proceedings of the 24th International Conference on Machine Learning
, pp. 807-814
-
-
Shalev-Shwartz, S.1
Singer, Y.2
Srebro, N.3
-
45
-
-
65449144451
-
Get another label? Improving data quality and data mining using multiple, noisy labelers
-
Las Vegas, NV
-
V. S. Sheng, F. Provost, and P. G. Ipeirotis. Get another label? improving data quality and data mining using multiple, noisy labelers. In Proceedings of the 14th International Conference on Knowledge Discovery and Data mining, pages 614-622, Las Vegas, NV, 2008.
-
(2008)
Proceedings of the 14th International Conference on Knowledge Discovery and Data Mining
, pp. 614-622
-
-
Sheng, V.S.1
Provost, F.2
Ipeirotis, P.G.3
-
48
-
-
33749242620
-
Deterministic annealing for semi-supervised kernel machines
-
Pittsburgh, PA
-
V. Sindhwani, S. S. Keerthi, and O. Chapelle. Deterministic annealing for semi-supervised kernel machines. In Proceedings of the 23rd International Conference on Machine Learning, pages 841-848, Pittsburgh, PA, 2006.
-
(2006)
Proceedings of the 23rd International Conference on Machine Learning
, pp. 841-848
-
-
Sindhwani, V.1
Keerthi, S.S.2
Chapelle, O.3
-
49
-
-
33745776113
-
Large scale multiple kernel learning
-
S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf. Large scale multiple kernel learning. Journal of Machine Learning Research, 7:1531-1565, 2006.
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 1531-1565
-
-
Sonnenburg, S.1
Rätsch, G.2
Schäfer, C.3
Schölkopf, B.4
-
50
-
-
84858733268
-
Entropic graph regularization in non-parametric semi-supervised classification
-
Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, MIT Press, Cambridge, MA
-
A. Subramanya and J. Bilmes. Entropic graph regularization in non-parametric semi-supervised classification. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 1803-1811. MIT Press, Cambridge, MA, 2009.
-
(2009)
Advances in Neural Information Processing Systems
, vol.22
, pp. 1803-1811
-
-
Subramanya, A.1
Bilmes, J.2
-
51
-
-
77958587970
-
Multi-label learning with weak label
-
Atlanta, GA
-
Y.-Y. Sun, Y. Zhang, and Z.-H. Zhou. Multi-label learning with weak label. In Proceedings of the 24th AAAI Conference on Artificial Intelligence, pages 593-598, Atlanta, GA, 2010.
-
(2010)
Proceedings of the 24th AAAI Conference on Artificial Intelligence
, pp. 593-598
-
-
Sun, Y.-Y.1
Zhang, Y.2
Zhou, Z.-H.3
-
53
-
-
24944537843
-
Large margin methods for structured and interdependent output variables
-
I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6(2):1453, 2006.
-
(2006)
Journal of Machine Learning Research
, vol.6
, Issue.2
, pp. 1453
-
-
Tsochantaridis, I.1
Joachims, T.2
Hofmann, T.3
Altun, Y.4
-
54
-
-
84864041449
-
Generalized maximum margin clustering and unsupervised kernel learning
-
B. Schölkopf, J. Platt, and T. Hoffman, editors, MIT Press, Cambridge, MA
-
H. Valizadegan and R. Jin. Generalized maximum margin clustering and unsupervised kernel learning. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 1417-1424. MIT Press, Cambridge, MA, 2007.
-
(2007)
Advances in Neural Information Processing Systems 19
, pp. 1417-1424
-
-
Valizadegan, H.1
Jin, R.2
-
56
-
-
56449086489
-
Adaptive p-posterior mixture-model kernels for multiple instance learning
-
Helsinki, Finland
-
H. Y. Wang, Q. Yang, and H. Zha. Adaptive p-posterior mixture-model kernels for multiple instance learning. In Proceedings of the 25th International Conference on Machine Learning, pages 1136-1143, Helsinki, Finland, 2008.
-
(2008)
Proceedings of the 25th International Conference on Machine Learning
, pp. 1136-1143
-
-
Wang, H.Y.1
Yang, Q.2
Zha, H.3
-
58
-
-
84898944155
-
Maximum margin clustering
-
L. K. Saul, Y. Weiss, and L. Bottou, editors, MIT Press, Cambridge, MA
-
L. Xu, J. Neufeld, B. Larson, and D. Schuurmans. Maximum margin clustering. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 1537-1544. MIT Press, Cambridge, MA, 2005.
-
(2005)
Advances in Neural Information Processing Systems
, vol.17
, pp. 1537-1544
-
-
Xu, L.1
Neufeld, J.2
Larson, B.3
Schuurmans, D.4
-
60
-
-
84863385308
-
An extended level method for efficient multiple kernel learning
-
D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, MIT Press, Cambridge, MA
-
Z. Xu, R. Jin, I. King, and M. R. Lyu. An extended level method for efficient multiple kernel learning. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, pages 1825-1832. MIT Press, Cambridge, MA, 2009.
-
(2009)
Advances in Neural Information Processing Systems
, vol.21
, pp. 1825-1832
-
-
Xu, Z.1
Jin, R.2
King, I.3
Lyu, M.R.4
-
61
-
-
77956547440
-
Simple and efficient multiple kernel learning by group lasso
-
Haifa, Israel
-
Z. Xu, R. Jin, H. Yang, I. King, and M. Lyu. Simple and efficient multiple kernel learning by group lasso. In Proceedings of 27th International Conference on Machine Learning, pages 1-8, Haifa, Israel, 2010.
-
(2010)
Proceedings of 27th International Conference on Machine Learning
, pp. 1-8
-
-
Xu, Z.1
Jin, R.2
Yang, H.3
King, I.4
Lyu, M.5
-
62
-
-
84896061460
-
Multi-instance multi-label learning with weak label
-
S.-J. Yang, Y. Jiang, and Z.-H. Zhou. Multi-instance multi-label learning with weak label. In Proceedings of 23rd International Joint Conference on Artificial Intelligence, Beijing, China, 2013.
-
Proceedings of 23rd International Joint Conference on Artificial Intelligence, Beijing, China, 2013
-
-
Yang, S.-J.1
Jiang, Y.2
Zhou, Z.-H.3
-
63
-
-
34547987546
-
Maximum margin clustering made practical
-
Corvallis, OR
-
K. Zhang, I. W. Tsang, and J. T. Kwok. Maximum margin clustering made practical. In Proceedings of the 24th International Conference on Machine Learning, pages 1119-1126, Corvallis, OR, 2007.
-
(2007)
Proceedings of the 24th International Conference on Machine Learning
, pp. 1119-1126
-
-
Zhang, K.1
Tsang, I.W.2
Kwok, J.T.3
-
64
-
-
71149121119
-
Prototype vector machine for large scale semi-supervised learning
-
Montreal, Canada
-
K. Zhang, J. T. Kwok, and B. Parvin. Prototype vector machine for large scale semi-supervised learning. In Proceedings of the 26th International Conference on Machine Learning, pages 1233-1240, Montreal, Canada, 2009a.
-
(2009)
Proceedings of the 26th International Conference on Machine Learning
, pp. 1233-1240
-
-
Zhang, K.1
Kwok, J.T.2
Parvin, B.3
-
65
-
-
67349089521
-
Maximum margin clustering made practical
-
K. Zhang, I. W. Tsang, and J. T. Kwok. Maximum margin clustering made practical. IEEE Transactions on Neural Networks, 20(4):583-596, 2009b.
-
(2009)
IEEE Transactions on Neural Networks
, vol.20
, Issue.4
, pp. 583-596
-
-
Zhang, K.1
Tsang, I.W.2
Kwok, J.T.3
-
66
-
-
84898999828
-
EM-DD: An improved multiple-instance learning technique
-
T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, MIT Press, Cambridge, MA
-
Q. Zhang and S. A. Goldman. EM-DD: An improved multiple-instance learning technique. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 1073-1080. MIT Press, Cambridge, MA, 2002.
-
(2002)
Advances in Neural Information Processing Systems
, vol.14
, pp. 1073-1080
-
-
Zhang, Q.1
Goldman, S.A.2
-
67
-
-
52649138409
-
Efficient maximum margin clustering via cutting plane algorithm
-
Atlanta, GA
-
B. Zhao, F. Wang, and C. Zhang. Efficient maximum margin clustering via cutting plane algorithm. In Proceedings of the 8th International Conference on Data Mining, pages 751-762, Atlanta, GA, 2008.
-
(2008)
Proceedings of the 8th International Conference on Data Mining
, pp. 751-762
-
-
Zhao, B.1
Wang, F.2
Zhang, C.3
-
68
-
-
77956708689
-
Semi-supervised learning by disagreement
-
Z.-H. Zhou and M. Li. Semi-supervised learning by disagreement. Knowledge and Information Systems, 24(3):415-439, 2010.
-
(2010)
Knowledge and Information Systems
, vol.24
, Issue.3
, pp. 415-439
-
-
Zhou, Z.-H.1
Li, M.2
-
70
-
-
84864028262
-
Multi-instance multi-label learning with application to scene classification
-
B. Schölkopf, J. Platt, and T. Hofmann, editors, MIT Press, Cambridge, MA
-
Z.-H. Zhou and M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In B. Schölkopf, J. Platt, and T. Hofmann, editors, Advances in Neural Information Processing Systems 19, pages 1609-1616. MIT Press, Cambridge, MA, 2007.
-
(2007)
Advances in Neural Information Processing Systems
, vol.19
, pp. 1609-1616
-
-
Zhou, Z.-H.1
Zhang, M.-L.2
-
71
-
-
33745613010
-
Locating regions of interest in CBIR with multi-instance learning techniques
-
Sydney, Australia
-
Z.-H. Zhou, X.-B. Xue, and Y. Jiang. Locating regions of interest in CBIR with multi-instance learning techniques. In Proceedings of the 18th Australian Joint Conference on Artificial Intelligence, pages 92-101, Sydney, Australia, 2005.
-
(2005)
Proceedings of the 18th Australian Joint Conference on Artificial Intelligence
, pp. 92-101
-
-
Zhou, Z.-H.1
Xue, X.-B.2
Jiang, Y.3
-
72
-
-
80955134248
-
Multi-instance multi-label learning
-
Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, and Y.-F. Li. Multi-instance multi-label learning. Artificial Intelligence, 176(1):2291-2320, 2012.
-
(2012)
Artificial Intelligence
, vol.176
, Issue.1
, pp. 2291-2320
-
-
Zhou, Z.-H.1
Zhang, M.-L.2
Huang, S.-J.3
Li, Y.-F.4
-
73
-
-
33745456231
-
-
Technical report, Computer Science, University of Wisconsin-Madison
-
X. Zhu. Semi-supervised learning literature survey. Technical report, Computer Science, University of Wisconsin-Madison, 2006.
-
(2006)
Semi-supervised Learning Literature Survey
-
-
Zhu, X.1
|