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Volumn 22, Issue , 2012, Pages 779-787

Max-margin min-entropy models

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); IMAGE CLASSIFICATION; LEARNING SYSTEMS; PROBABILITY DISTRIBUTIONS;

EID: 84899875815     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (12)

References (26)
  • 1
    • 45449115390 scopus 로고    scopus 로고
    • Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences
    • M. Berger, G. Badis, A. Gehrke, and S. Talukder et al. Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences. Cell, 2008.
    • (2008) Cell
    • Berger, M.1    Badis, G.2    Gehrke, A.3    Talukder, S.4
  • 3
    • 33645146449 scopus 로고    scopus 로고
    • Histograms of oriented gradients for human detection
    • N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
    • (2005) CVPR
    • Dalal, N.1    Triggs, B.2
  • 5
    • 51949101231 scopus 로고    scopus 로고
    • A discriminatively trained, multiscale, deformable part model
    • P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In CVPR, 2008.
    • (2008) CVPR
    • Felzenszwalb, P.1    McAllester, D.2    Ramanan, D.3
  • 7
    • 0002453010 scopus 로고
    • Quantification method in classification processes: Concept of structural α-entropy
    • J. Havrada and F. Charvat. Quantification method in classification processes: Concept of structural α-entropy. Kybernetika, 1967.
    • (1967) Kybernetika
    • Havrada, J.1    Charvat, F.2
  • 8
    • 67349237188 scopus 로고    scopus 로고
    • Shape-based object localization for descriptive classification
    • G. Heitz, G. Elidan, B. Packer, and D. Koller. Shape-based object localization for descriptive classification. IJCV, 2009.
    • (2009) IJCV
    • Heitz, G.1    Elidan, G.2    Packer, B.3    Koller, D.4
  • 12
    • 14344265736 scopus 로고    scopus 로고
    • Feature selection and dualities in maximum entropy discrimination
    • T. Jebara and T. Jaakkola. Feature selection and dualities in maximum entropy discrimination. In UAI, 2000.
    • (2000) UAI
    • Jebara, T.1    Jaakkola, T.2
  • 14
    • 85161967298 scopus 로고    scopus 로고
    • Selfpaced learning for latent variable models
    • M. P. Kumar, B. Packer, and D. Koller. Selfpaced learning for latent variable models. In NIPS, 2010.
    • (2010) NIPS
    • Kumar, M.P.1    Packer, B.2    Koller, D.3
  • 16
    • 0002788893 scopus 로고    scopus 로고
    • A view of the em algorithm that justifies incremental, sparse, and other variants
    • M. Jordan, editor, MIT Press
    • R. Neal and G. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. InM. Jordan, editor, Learning in Graphical Models. MIT Press, 1999.
    • (1999) Learning in Graphical Models
    • Neal, R.1    Hinton, G.2
  • 17
    • 0003143622 scopus 로고
    • Diversity and dissimilarity coefficients: A unified approach
    • C. Rao. Diversity and dissimilarity coefficients: A unified approach. Theoretical Population Biology, 1982.
    • (1982) Theoretical Population Biology
    • Rao, C.1
  • 19
    • 31844453031 scopus 로고    scopus 로고
    • Expectation maximization algorithms for conditional likelihoods
    • J. Salojarvi, K. Puolamaki, and S. Kaski. Expectation maximization algorithms for conditional likelihoods. In ICML, 2005.
    • (2005) ICML
    • Salojarvi, J.1    Puolamaki, K.2    Kaski, S.3
  • 20
    • 48849117633 scopus 로고    scopus 로고
    • Pegasos: Primal estimated sub-gradient solver for SVM
    • S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for SVM. In ICML, 2009.
    • (2009) ICML
    • Shalev-Shwartz, S.1    Singer, Y.2    Srebro, N.3
  • 22
    • 0001953676 scopus 로고
    • Maximum likelihood theory for incomplete data from an exponential family
    • R. Sundberg. Maximum likelihood theory for incomplete data from an exponential family. Scandinavian Journal of Statistics, 1974.
    • (1974) Scandinavian Journal of Statistics
    • Sundberg, R.1
  • 24
    • 14344250451 scopus 로고    scopus 로고
    • Support vector machine learning for interdependent and structured output spaces
    • I. Tsochantaridis, T. Hofmann, Y. Altun, and T. Joachims. Support vector machine learning for interdependent and structured output spaces. In ICML, 2004.
    • (2004) ICML
    • Tsochantaridis, I.1    Hofmann, T.2    Altun, Y.3    Joachims, T.4
  • 25
    • 71149086466 scopus 로고    scopus 로고
    • Learning structural SVMs with latent variables
    • C.-N. Yu and T. Joachims. Learning structural SVMs with latent variables. In ICML, 2009.
    • (2009) ICML
    • Yu, C.-N.1    Joachims, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.