-
1
-
-
3042597440
-
Learning multi-label scene classification
-
M. R. Boutell, J. Luo, X. Shen, and C. M. Brown. Learning multi-label scene classification. Pattern Recognition, 37(9):1757-1771, 2004.
-
(2004)
Pattern Recognition
, vol.37
, Issue.9
, pp. 1757-1771
-
-
Boutell, M.R.1
Luo, J.2
Shen, X.3
Brown, C.M.4
-
3
-
-
68949141664
-
Combining instance-based learning and logistic regression for multilabel classification
-
W. Cheng and E. Hüllermeier. Combining instance-based learning and logistic regression for multilabel classification. Machine Learning, 76(2-3):211-225, 2009.
-
(2009)
Machine Learning
, vol.76
, Issue.2-3
, pp. 211-225
-
-
Cheng, W.1
Hüllermeier, E.2
-
4
-
-
84943242305
-
Knowledge discovery in multi-label phenotype data
-
L. D. Raedt and A. Siebes, editors Springer, Berlin
-
A. Clare and R. D. King. Knowledge discovery in multi-label phenotype data. In L. D. Raedt and A. Siebes, editors, Lecture Notes in Computer Science 2168, pages 42-53. Springer, Berlin, 2001.
-
(2001)
Lecture Notes in Computer Science 2168
, pp. 42-53
-
-
Clare, A.1
King, R.D.2
-
5
-
-
8344282787
-
Learning multi-label altenating decision tree from texts and data
-
P. Perner and A. Rosenfeld, editors Springer, Berlin
-
F. D. Comité, R. Gilleron, and M. Tommasi. Learning multi-label altenating decision tree from texts and data. In P. Perner and A. Rosenfeld, editors, Lecture Notes in Computer Science 2734, pages 35-49. Springer, Berlin, 2003.
-
(2003)
Lecture Notes in Computer Science 2734
, pp. 35-49
-
-
Comité, F.D.1
Gilleron, R.2
Tommasi, M.3
-
7
-
-
76649137444
-
A kernel method for multi-labelled classification
-
T. G. Dietterich, S. Becker, and Z. Ghahramani, editors MIT Press, Cambridge, MA
-
A. Elisseeff and J. Weston. A kernel method for multi-labelled classification. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 681-687. MIT Press, Cambridge, MA, 2002.
-
(2002)
Advances in Neural Information Processing Systems 14
, pp. 681-687
-
-
Elisseeff, A.1
Weston, J.2
-
8
-
-
52949105710
-
Multilabel classification via calibrated label ranking
-
J. Fürnkranz, E. Hüllermeier, E. L. Mencía, and K. Brinker. Multilabel classification via calibrated label ranking. Machine Learning, 73(2):133-153, 2008.
-
(2008)
Machine Learning
, vol.73
, Issue.2
, pp. 133-153
-
-
Fürnkranz, J.1
Hüllermeier, E.2
Mencía, E.L.3
Brinker, K.4
-
10
-
-
7444230008
-
Discriminative methods for multi-labeled classification
-
H. Dai, R. Srikant, and C. Zhang, editors Springer, Berlin
-
S. Godbole and S. Sarawagi. Discriminative methods for multi-labeled classification. In H. Dai, R. Srikant, and C. Zhang, editors, Lecture Notes in Artificial Intelligence 3056, pages 22-30. Springer, Berlin, 2004.
-
(2004)
Lecture Notes in Artificial Intelligence 3056
, pp. 22-30
-
-
Godbole, S.1
Sarawagi, S.2
-
11
-
-
65449189832
-
Extracting shared subspace for multi-label classification
-
Las Vegas, NV
-
S. Ji, L. Tang, S. Yu, and J. Ye. Extracting shared subspace for multi-label classification. In Proceedings of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 381-389, Las Vegas, NV, 2008.
-
(2008)
Proceedings of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
, pp. 381-389
-
-
Ji, S.1
Tang, L.2
Yu, S.3
Ye, J.4
-
16
-
-
37849015906
-
Correlative multi-label video annotation
-
Augsburg, Germany
-
G.-J. Qi, X.-S. Hua, Y. Rui, J. Tang, T. Mei, and H.-J. Zhang. Correlative multi-label video annotation. In Proceedings of the 15th ACM International Conference on Multimedia, pages 17-26, Augsburg, Germany, 2007.
-
(2007)
Proceedings of the 15th ACM International Conference on Multimedia
, pp. 17-26
-
-
Qi, G.-J.1
Hua, X.-S.2
Rui, Y.3
Tang, J.4
Mei, T.5
Zhang, H.-J.6
-
17
-
-
67049088703
-
Multi-label classification using ensembles of pruned sets
-
Pisa, Italy
-
J. Read, B. Pfahringer, and G. Holmes. Multi-label classification using ensembles of pruned sets. In Proceedings of the 9th IEEE International Conference on Data Mining, pages 995-1000, Pisa, Italy, 2008.
-
(2008)
Proceedings of the 9th IEEE International Conference on Data Mining
, pp. 995-1000
-
-
Read, J.1
Pfahringer, B.2
Holmes, G.3
-
18
-
-
70349968175
-
Classifier chains for multi-label classification
-
W. Buntine, M. Grobelnik, and J. Shawe-Taylor, editors Springer, Berlin
-
J. Read, B. Pfahringer, G. Holmes, and E. Frank. Classifier chains for multi-label classification. In W. Buntine, M. Grobelnik, and J. Shawe-Taylor, editors, Lecture Notes in Artificial Intelligence 5782, pages 254-269. Springer, Berlin, 2009.
-
(2009)
Lecture Notes in Artificial Intelligence 5782
, pp. 254-269
-
-
Read, J.1
Pfahringer, B.2
Holmes, G.3
Frank, E.4
-
19
-
-
0033905095
-
Boostexter: A boosting-based system for text categorization
-
R. E. Schapire and Y. Singer. Boostexter: a boosting-based system for text categorization. Machine Learning, 39(2/3):135-168, 2000.
-
(2000)
Machine Learning
, vol.39
, Issue.2-3
, pp. 135-168
-
-
Schapire, R.E.1
Singer, Y.2
-
20
-
-
33751407959
-
Computational inference of neural information flow networks
-
V. Smith, J. Yu, T. Smulders, A. Hartemink, and E. Jarvis. Computational inference of neural information flow networks. PLoS Computational Biology, 2(11):1436-1449, 2006.
-
(2006)
PLoS Computational Biology
, vol.2
, Issue.11
, pp. 1436-1449
-
-
Smith, V.1
Yu, J.2
Smulders, T.3
Hartemink, A.4
Jarvis, E.5
-
21
-
-
77956163078
-
Mining multi-label data
-
O. Maimon and L. Rokach, editors Springer, Berlin
-
G. Tsoumakas, I. Katakis, and I. Vlahavas. Mining multi-label data. In O. Maimon and L. Rokach, editors, Data Mining and Knowledge Discovery Handbook. Springer, Berlin, 2010.
-
(2010)
Data Mining and Knowledge Discovery Handbook
-
-
Tsoumakas, G.1
Katakis, I.2
Vlahavas, I.3
-
22
-
-
38049123909
-
Random k-labelsets: An ensemble method for multilabel classification
-
J. N. Kok, J. Koronacki, R. L. de Mantaras, S. Matwin, D. Mladenič, and A. Skowron, editors Springer, Berlin
-
G. Tsoumakas and I. Vlahavas. Random k-labelsets: an ensemble method for multilabel classification. In J. N. Kok, J. Koronacki, R. L. de Mantaras, S. Matwin, D. Mladenič, and A. Skowron, editors, Lecture Notes in Artificial Intelligence 4701, pages 406-417. Springer, Berlin, 2007.
-
(2007)
Lecture Notes in Artificial Intelligence 4701
, pp. 406-417
-
-
Tsoumakas, G.1
Vlahavas, I.2
-
23
-
-
84877995077
-
Tutorial on learning from multi-label data
-
Bled, Slovenia
-
G. Tsoumakas, M.-L. Zhang, and Z.-H. Zhou. Tutorial on learning from multi-label data [http://www.ecmlpkdd2009.net/wp-content/uploads/2009/08/ learning-from-multi-label-data.pdf]. In ECML/PKDD 2009, Bled, Slovenia, 2009.
-
(2009)
ECML/PKDD 2009
-
-
Tsoumakas, G.1
Zhang, M.-L.2
Zhou, Z.-H.3
-
24
-
-
84898954552
-
Parametric mixture models for multi-label text
-
S. Becker, S. Thrun, and K. Obermayer, editors MIT Press, Cambridge, MA
-
N. Ueda and K. Saito. Parametric mixture models for multi-label text. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 721-728. MIT Press, Cambridge, MA, 2003.
-
(2003)
Advances in Neural Information Processing Systems 15
, pp. 721-728
-
-
Ueda, N.1
Saito, K.2
-
25
-
-
36849011561
-
Model-shared subspace boosting for multi-label classification
-
San Jose, CA
-
R. Yan, J. Tešić, and J. R. Smith. Model-shared subspace boosting for multi-label classification. In Proceedings of the 13th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 834-843, San Jose, CA, 2007.
-
(2007)
Proceedings of the 13th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
, pp. 834-843
-
-
Yan, R.1
Tešić, J.2
Smith, J.R.3
-
27
-
-
70349939643
-
Causality discovery with additive disturbances: An information- theoretical perspective
-
W. Buntine, M. Grobelnik, and J. Shawe-Taylor, editors Springer, Berlin
-
K. Zhang and A. Hyvärinen. Causality discovery with additive disturbances: An information-theoretical perspective. In W. Buntine, M. Grobelnik, and J. Shawe-Taylor, editors, Lecture Notes in Artificial Intelligence 5782, pages 570-585. Springer, Berlin, 2009.
-
(2009)
Lecture Notes in Artificial Intelligence 5782
, pp. 570-585
-
-
Zhang, K.1
Hyvärinen, A.2
-
28
-
-
33748366796
-
Multilabel neural networks with applications to functional genomics and text categorization
-
M.-L. Zhang and Z.-H. Zhou. Multilabel neural networks with applications to functional genomics and text categorization. IEEE Transactions on Knowledge and Data Engineering, 18(10):1338-1351, 2006.
-
(2006)
IEEE Transactions on Knowledge and Data Engineering
, vol.18
, Issue.10
, pp. 1338-1351
-
-
Zhang, M.-L.1
Zhou, Z.-H.2
-
29
-
-
33947681316
-
ML-kNN: A lazy learning approach to multi-label learning
-
M.-L. Zhang and Z.-H. Zhou. ML-kNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7):2038-2048, 2007.
-
(2007)
Pattern Recognition
, vol.40
, Issue.7
, pp. 2038-2048
-
-
Zhang, M.-L.1
Zhou, Z.-H.2
-
30
-
-
84885572482
-
Multi-labelled classification using maximum entropy method
-
Salvador, Brazil
-
S. Zhu, X. Ji, W. Xu, and Y. Gong. Multi-labelled classification using maximum entropy method. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 274-281, Salvador, Brazil, 2005.
-
(2005)
Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
, pp. 274-281
-
-
Zhu, S.1
Ji, X.2
Xu, W.3
Gong, Y.4
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