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Volumn , Issue , 2013, Pages

Robust Bloom filters for large multilabel classification tasks

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[No Author keywords available]

Indexed keywords

INFERENCE ENGINES;

EID: 84898977831     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (91)

References (17)
  • 2
    • 0014814325 scopus 로고
    • Space/time trade-offs in hash coding with allowable errors
    • B. H. Bloom. Space/time trade-offs in hash coding with allowable errors. Commun. ACM, 13(7):422-426, 1970.
    • (1970) Commun ACM , vol.13 , Issue.7 , pp. 422-426
    • Bloom, B.H.1
  • 4
    • 84877784525 scopus 로고    scopus 로고
    • Feature-aware label space dimension reduction for multi-label classification
    • Y.-N. Chen and H.-T. Lin. Feature-aware label space dimension reduction for multi-label classification. In NIPS, pages 1538-1546, 2012.
    • (2012) NIPS , pp. 1538-1546
    • Chen, Y.-N.1    Lin, H.-T.2
  • 5
    • 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
  • 6
    • 77955214744 scopus 로고    scopus 로고
    • A new analysis of the false positive rate of a bloom filter
    • Oct
    • K. Christensen, A. Roginsky, and M. Jimeno. A new analysis of the false positive rate of a bloom filter. Inf. Process. Lett., 110(21):944-949, Oct. 2010.
    • (2010) Inf. Process. Lett. , vol.110 , Issue.21 , pp. 944-949
    • Christensen, K.1    Roginsky, A.2    Jimeno, M.3
  • 8
    • 77956522919 scopus 로고    scopus 로고
    • Bayes optimal multilabel classification via probabilistic classifier chains
    • K. Dembczynski, W. Cheng, and E. Hüllermeier. Bayes optimal multilabel classification via probabilistic classifier chains. In ICML, pages 279-286, 2010.
    • (2010) ICML , pp. 279-286
    • Dembczynski, K.1    Cheng, W.2    Hüllermeier, E.3
  • 9
    • 84865223006 scopus 로고    scopus 로고
    • On label dependence and loss minimization in multi-label classification
    • K. Dembczynski,W.Waegeman,W. Cheng, and E. Hüllermeier. On label dependence and loss minimization in multi-label classification. Machine Learning, 88(1-2):5-45, 2012.
    • (2012) Machine Learning , vol.88 , Issue.1-2 , pp. 5-45
    • Dembczynski, W.1    Waegeman, W.2    Cheng, W.3    Hüllermeier, E.4
  • 12
    • 77956528679 scopus 로고    scopus 로고
    • Multi-label prediction via compressed sensing
    • D. Hsu, S. Kakade, J. Langford, and T. Zhang. Multi-label prediction via compressed sensing. In NIPS, pages 772-780, 2009.
    • (2009) NIPS , pp. 772-780
    • Hsu, D.1    Kakade, S.2    Langford, J.3    Zhang, T.4
  • 13
    • 84898968611 scopus 로고    scopus 로고
    • RCV1. RCV1 Dataset, http://www.daviddlewis.com/resources/testcollections/ rcv1/.
    • RCV1 Dataset
  • 15
    • 84867116137 scopus 로고    scopus 로고
    • Multilabel classification with principal label space transformation
    • F. Tai and H.-T. Lin. Multilabel classification with principal label space transformation. Neural Computation, 24(9):2508-2542, 2012.
    • (2012) Neural Computation , vol.24 , Issue.9 , pp. 2508-2542
    • Tai, F.1    Lin, H.-T.2
  • 17
    • 84898928594 scopus 로고    scopus 로고
    • Wikipedia
    • Wikipedia. Wikipedia Dataset, http://lshtc.iit.demokritos.gr/.
    • Wikipedia Dataset


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.