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Volumn , Issue , 2010, Pages 999-1007

Multi-label learning by exploiting label dependency

Author keywords

Algorithms

Indexed keywords

BAYESIAN NETWORK STRUCTURE; DATA SETS; FEATURE SETS; LABEL SETS; LEARNING PROCESS; MULTI-LABEL; NETWORK STRUCTURES; TRAINING EXAMPLE;

EID: 77956201769     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1835804.1835930     Document Type: Conference Paper
Times cited : (428)

References (30)
  • 1
    • 3042597440 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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
  • 10
    • 7444230008 scopus 로고    scopus 로고
    • 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
  • 18
    • 70349968175 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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
  • 22
    • 38049123909 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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
  • 27
    • 70349939643 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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 scopus 로고    scopus 로고
    • 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


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.