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Volumn , Issue , 2011, Pages

Maximum margin multi-label structured prediction

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONVEX OPTIMIZATION; FORECASTING; PATTERN MATCHING;

EID: 85162413154     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (32)

References (35)
  • 1
    • 0142192295 scopus 로고    scopus 로고
    • Conditional random fields: Probabilistic models for segmenting and labeling sequence data
    • J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001.
    • (2001) ICML
    • Lafferty, J.D.1    McCallum, A.2    Pereira, F.C.N.3
  • 3
    • 24944537843 scopus 로고    scopus 로고
    • 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. JMLR, 6, 2006.
    • (2006) JMLR , vol.6
    • Tsochantaridis, I.1    Joachims, T.2    Hofmann, T.3    Altun, Y.4
  • 4
    • 69549111057 scopus 로고    scopus 로고
    • Cutting-plane training of structural SVMs
    • T. Joachims, T. Finley, and C. N. J. Yu. Cutting-plane training of structural SVMs. Machine Learning, 77(1), 2009.
    • (2009) Machine Learning , vol.77 , Issue.1
    • Joachims, T.1    Finley, T.2    Yu, C.N.J.3
  • 7
    • 78049347622 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, 2011.
    • (2011) ICML
    • Dembczynski, K.1    Cheng, W.2    Hüllermeier, E.3
  • 8
    • 50149118484 scopus 로고    scopus 로고
    • Generalization bounds and consistency for structured labeling
    • G. Bakir, T. Hofmann, B. Schölkopf, A.J. Smola, and B. Taskar, editors. MIT Press
    • D. McAllester. Generalization bounds and consistency for structured labeling. In G. Bakir, T. Hofmann, B. Schölkopf, A.J. Smola, and B. Taskar, editors, Predicting Structured Data. MIT Press, 2007.
    • (2007) Predicting Structured Data
    • McAllester, D.1
  • 9
    • 0006776658 scopus 로고    scopus 로고
    • An efficient algorithm for finding the M most probable configurations in probabilistic expert systems
    • D. Nilsson. An efficient algorithm for finding the M most probable configurations in probabilistic expert systems. Statistics and Computing, 8(2), 1998.
    • (1998) Statistics and Computing , vol.8 , Issue.2
    • Nilsson, D.1
  • 10
    • 33846324463 scopus 로고    scopus 로고
    • Finding the M most probable configurations using loopy belief propagation
    • C. Yanover and Y. Weiss. Finding the M most probable configurations using loopy belief propagation. In NIPS, 2004.
    • (2004) NIPS
    • Yanover, C.1    Weiss, Y.2
  • 11
    • 78649417672 scopus 로고    scopus 로고
    • An LP View of the M-best MAP problem
    • M. Fromer and A. Globerson. An LP View of the M-best MAP problem. In NIPS, 2009.
    • (2009) NIPS
    • Fromer, M.1    Globerson, A.2
  • 12
    • 80054894097 scopus 로고    scopus 로고
    • C4: Exploring multiple solutions in graphical models by cluster sampling
    • J. Porway and S.-C. Zhu. C4: Exploring multiple solutions in graphical models by cluster sampling. PAMI, 33(9), 2011.
    • (2011) PAMI , vol.33 , Issue.9
    • Porway, J.1    Zhu, S.-C.2
  • 13
    • 70350619001 scopus 로고    scopus 로고
    • Learning to localize objects with structured output regression
    • M. B. Blaschko and C. H. Lampert. Learning to localize objects with structured output regression. In ECCV, 2008.
    • (2008) ECCV
    • Blaschko, M.B.1    Lampert, C.H.2
  • 14
    • 70049083618 scopus 로고    scopus 로고
    • Sequence labelling SVMs trained in one pass
    • A. Bordes, N. Usunier, and L. Bottou. Sequence labelling SVMs trained in one pass. ECMLPKDD, 2008.
    • (2008) ECMLPKDD
    • Bordes, A.1    Usunier, N.2    Bottou, L.3
  • 16
    • 0000636553 scopus 로고    scopus 로고
    • Text categorization with support vector machines: Learning with many relevant features
    • T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In ECML, 1998.
    • (1998) ECML
    • Joachims, T.1
  • 17
    • 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), 2000.
    • (2000) Machine Learning , vol.39 , Issue.2-3
    • Schapire, R.E.1    Singer, Y.2
  • 18
    • 36448983903 scopus 로고    scopus 로고
    • A support vector method for optimizing average precision
    • Y. Yue, T. Finley, F. Radlinski, and T. Joachims. A support vector method for optimizing average precision. In ACM SIGIR, 2007.
    • (2007) ACM SIGIR
    • Yue, Y.1    Finley, T.2    Radlinski, F.3    Joachims, T.4
  • 19
    • 68949149733 scopus 로고    scopus 로고
    • On structured output training: Hard cases and an efficient alternative
    • T. Gärtner and S. Vembu. On structured output training: Hard cases and an efficient alternative. Machine Learning, 76(2):227-242, 2009.
    • (2009) Machine Learning , vol.76 , Issue.2 , pp. 227-242
    • Gärtner, T.1    Vembu, S.2
  • 20
    • 56449130129 scopus 로고    scopus 로고
    • Predicting diverse subsets using structural SVMs
    • Y. Yue and T. Joachims. Predicting diverse subsets using structural SVMs. In ICML, 2008.
    • (2008) ICML
    • Yue, Y.1    Joachims, T.2
  • 21
    • 65449189832 scopus 로고    scopus 로고
    • Extracting shared subspaces for multi-label classification. in
    • S. Ji, L. Tang, S. Yu, and J. Ye. Extracting shared subspaces for multi-label classification. In ACM SIGKDD, 2008.
    • (2008) ACM SIGKDD
    • Ji, S.1    Tang, L.2    Yu, S.3    Ye, J.4
  • 22
    • 77958600377 scopus 로고    scopus 로고
    • Multi-label prediction via sparse infinite CCA
    • P. Rai and H. Daumé III. Multi-label prediction via sparse infinite CCA. In NIPS, 2009.
    • (2009) NIPS
    • Rai, P.1    Daumé III, H.2
  • 24
    • 80053440655 scopus 로고    scopus 로고
    • Multi-label classification on tree- and DAG-structured hierarchies
    • W. Bi and J. Kwok. Multi-label classification on tree- and DAG-structured hierarchies. In ICML, 2011.
    • (2011) ICML
    • Bi, W.1    Kwok, J.2
  • 25
    • 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, 2009.
    • (2009) NIPS
    • Hsu, D.1    Kakade, S.2    Langford, J.3    Zhang, T.4
  • 26
    • 74849083829 scopus 로고    scopus 로고
    • Effective and efficient multilabel classification in domains with large number of labels
    • G. Tsoumakas, I. Katakis, and I. Vlahavas. Effective and efficient multilabel classification in domains with large number of labels. In ECMLPKDD, 2008.
    • (2008) ECMLPKDD
    • Tsoumakas, G.1    Katakis, I.2    Vlahavas, I.3
  • 27
    • 72449124267 scopus 로고    scopus 로고
    • Structured prediction by joint kernel support estimation
    • C. H. Lampert and M. B. Blaschko. Structured prediction by joint kernel support estimation. Machine Learning, 77(2-3), 2009.
    • (2009) Machine Learning , vol.77 , Issue.2-3
    • Lampert, C.H.1    Blaschko, M.B.2
  • 29
    • 33745768424 scopus 로고    scopus 로고
    • Kernel-based learning of hierarchical multilabel classification models
    • J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor. Kernel-based learning of hierarchical multilabel classification models. JMLR, 7, 2006.
    • (2006) JMLR , vol.7
    • Rousu, J.1    Saunders, C.2    Szedmak, S.3    Shawe-Taylor, J.4
  • 30
    • 84860615448 scopus 로고    scopus 로고
    • On taxonomies for multi-class image categorization
    • A. Binder, K.-R. Müller, and M. Kawanabe. On taxonomies for multi-class image categorization. IJCV, 2011.
    • (2011) IJCV
    • Binder, A.1    Müller, K.-R.2    Kawanabe, M.3
  • 31
    • 18744367558 scopus 로고    scopus 로고
    • Hierarchical document categorization with support vector machines
    • L. Cai and T. Hofmann. Hierarchical document categorization with support vector machines. In ICKM, 2004.
    • (2004) ICKM
    • Cai, L.1    Hofmann, T.2
  • 32
    • 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, 2010.
    • (2010) ICML
    • Dembczynski, K.1    Cheng, W.2    Hüllermeier, E.3
  • 33
    • 70350621774 scopus 로고    scopus 로고
    • Efficient subwindow search: A branch and bound framework for object localization
    • C. H. Lampert, M. B. Blaschko, and T. Hofmann. Efficient subwindow search: A branch and bound framework for object localization. PAMI, 31(12), 2009.
    • (2009) PAMI , vol.31 , Issue.12
    • Lampert, C.H.1    Blaschko, M.B.2    Hofmann, T.3
  • 34
    • 12844249589 scopus 로고    scopus 로고
    • Learning to detect objects in images via a sparse, part-based representation
    • S. Agarwal, A. Awan, and D. Roth. Learning to detect objects in images via a sparse, part-based representation. PAMI, 26(11), 2004.
    • (2004) PAMI , vol.26 , Issue.11
    • Agarwal, S.1    Awan, A.2    Roth, D.3
  • 35
    • 77956009382 scopus 로고    scopus 로고
    • An efficient divide-and-conquer cascade for nonlinear object detection
    • C. H. Lampert. An efficient divide-and-conquer cascade for nonlinear object detection. In CVPR, 2010.
    • (2010) CVPR
    • Lampert, C.H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.