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Volumn , Issue , 2009, Pages 2844-2851

Co-training with noisy perceptual observations

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DATA STREAMS; OBJECT RECOGNITION; SEMI-SUPERVISED LEARNING; SENSOR NETWORKS;

EID: 70450194986     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206572     Document Type: Conference Paper
Times cited : (37)

References (18)
  • 1
    • 70450128164 scopus 로고    scopus 로고
    • Two-view feature generation model for semi-supervised learning
    • R. K. Ando and T. Zhang. Two-view feature gener- ation model for semi-supervised learning. In ICML, 2007.
    • (2007) ICML
    • Ando, R.K.1    Zhang, T.2
  • 2
    • 14344252374 scopus 로고    scopus 로고
    • Multiple kernel learning, conic duality, and the smo algorithm
    • F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the smo algorithm. In ICML, 2004.
    • (2004) ICML
    • Bach, F.R.1    Lanckriet, G.R.G.2    Jordan, M.I.3
  • 3
    • 0031620208 scopus 로고    scopus 로고
    • Combining labeled and unlabeled data with co-training
    • A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In COLT, 1998.
    • (1998) COLT
    • Blum, A.1    Mitchell, T.2
  • 5
    • 34547176689 scopus 로고    scopus 로고
    • Co-adaptation of audio-visual speech and gesture classifiers
    • November
    • C. M. Christoudias, K. Saenko, L.-P. Morency, and T. Darrell. Co-adaptation of audio-visual speech and gesture classifiers. In ICMI, November 2006.
    • (2006) ICMI
    • Christoudias, C.M.1    Saenko, K.2    Morency, L.-P.3    Darrell, T.4
  • 6
    • 80052418610 scopus 로고    scopus 로고
    • Multiview learning in the presence of view disagreement
    • C. M. Christoudias, R. Urtasun, and T. Darrell. Multiview learning in the presence of view disagreement. In UAI, 2008.
    • (2008) UAI
    • Christoudias, C.M.1    Urtasun, R.2    Darrell, T.3
  • 7
    • 85119383022 scopus 로고    scopus 로고
    • Unsupervised models for named entity classification
    • M. Collins and Y. Singer. Unsupervised models for named entity classification. In SIGDAT, 1999.
    • (1999) SIGDAT
    • Collins, M.1    Singer, Y.2
  • 8
    • 34247576789 scopus 로고    scopus 로고
    • The pyramid match kernel: E-cient learning with sets of features
    • K. Grauman and T. Darrell. The pyramid match kernel: E-cient learning with sets of features. JMLR, 2007.
    • (2007) JMLR
    • Grauman, K.1    Darrell, T.2
  • 10
    • 70450148854 scopus 로고    scopus 로고
    • Multi-view regression via canonical correlation analysis
    • S. M. Kakade and D. P. Foster. Multi-view regression via canonical correlation analysis. In COLT, 2007.
    • (2007) COLT
    • Kakade, S.M.1    Foster, D.P.2
  • 11
    • 0344982834 scopus 로고    scopus 로고
    • Unsupervised improvement of visual detectors using cotraining
    • A. Levin, P. Viola, and Y. Freund. Unsupervised improvement of visual detectors using cotraining. In ICCV, 2003.
    • (2003) ICCV
    • Levin, A.1    Viola, P.2    Freund, Y.3
  • 13
    • 3242746592 scopus 로고    scopus 로고
    • Adaptive view validation: A first step towards automatic view detection
    • I. Muslea, S. Minton, and C. A. Knoblock. Adaptive view validation: A first step towards automatic view detection. In ICML, 2002.
    • (2002) ICML
    • Muslea, I.1    Minton, S.2    Knoblock, C.A.3
  • 16
    • 33749243505 scopus 로고    scopus 로고
    • A co- regularization approach to semi-supervised learning with multiple views
    • V. Sindhwani, P. Niyogi, and M. Belkin. A co- regularization approach to semi-supervised learning with multiple views. In ICML, 2005.
    • (2005) ICML
    • Sindhwani, V.1    Niyogi, P.2    Belkin, M.3
  • 17
    • 33745161085 scopus 로고    scopus 로고
    • Semi-supervised cross feature learning for semantic concept detection in videos
    • R. Yan and M. Naphade. Semi-supervised cross feature learning for semantic concept detection in videos. In CVPR, 2005.
    • (2005) CVPR
    • Yan, R.1    Naphade, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.