메뉴 건너뛰기




Volumn 12, Issue , 2011, Pages 2649-2680

Bayesian co-training

Author keywords

Active sensing; Co training; Gaussian processes; Multi view learning; Semi supervised learning; Undirected graphical models

Indexed keywords

ACTIVE SENSING; CO-TRAINING; GAUSSIAN PROCESSES; GRAPHICAL MODEL; MULTI-VIEW LEARNING; SEMI-SUPERVISED LEARNING;

EID: 80555131692     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (145)

References (30)
  • 1
    • 26944451289 scopus 로고    scopus 로고
    • A PAC-style model for learning from labeled and unlabeled data
    • MIT Press
    • M. Balcan and A. Blum. A PAC-style model for learning from labeled and unlabeled data. In Semi-Supervised Learning, pages 111-126. MIT Press, 2006.
    • (2006) Semi-supervised Learning , pp. 111-126
    • Balcan, M.1    Blum, A.2
  • 2
    • 33750738734 scopus 로고    scopus 로고
    • Co-training and expansion: Towards bridging theory and practice
    • M. Balcan, A. Blum, and K. Yang. Co-training and expansion: Towards bridging theory and practice. In NIPS, 2004.
    • (2004) NIPS
    • Balcan, M.1    Blum, A.2    Yang, K.3
  • 3
    • 79958795748 scopus 로고    scopus 로고
    • Estimation of mixture models using Co-EM
    • S. Bickel and T. Scheffer. Estimation of mixture models using Co-EM. In ECML, 2005.
    • (2005) ECML
    • Bickel, S.1    Scheffer, T.2
  • 4
    • 80053642750 scopus 로고    scopus 로고
    • VOILA: Efficient feature-value acquisition for classification
    • M. Bilgic and L. Getoor. VOILA: Efficient feature-value acquisition for classification. In AAAI, 2007.
    • (2007) AAAI
    • Bilgic, M.1    Getoor, L.2
  • 5
    • 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
  • 6
    • 14344251008 scopus 로고    scopus 로고
    • Co-EM support vector learning
    • U. Brefeld and T. Scheffer. Co-EM support vector learning. In ICML, 2004.
    • (2004) ICML
    • Brefeld, U.1    Scheffer, T.2
  • 7
    • 34250767770 scopus 로고    scopus 로고
    • Efficient co-regularised least squares regression
    • U. Brefeld, T. Gärtner, T. Scheffer, and S. Wrobel. Efficient co-regularised least squares regression. In ICML, pages 137-144, 2006.
    • (2006) ICML , pp. 137-144
    • Brefeld, U.1    Gärtner, T.2    Scheffer, T.3    Wrobel, S.4
  • 9
    • 4043061882 scopus 로고    scopus 로고
    • Variational Bayesian model selection for mixture distributions
    • A. Corduneanu and C. M. Bishop. Variational Bayesian model selection for mixture distributions. In Workshop AI and Statistics, pages 27-34, 2001.
    • (2001) Workshop AI and Statistics , pp. 27-34
    • Corduneanu, A.1    Bishop, C.M.2
  • 10
  • 15
    • 3242750450 scopus 로고    scopus 로고
    • Email classification with co-training
    • University of Ottawa
    • S. Kiritchenko and S. Matwin. Email classification with co-training. Technical report, University of Ottawa, 2002.
    • (2002) Technical Report
    • Kiritchenko, S.1    Matwin, S.2
  • 16
    • 41549146576 scopus 로고    scopus 로고
    • Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies
    • A. Krause, A. Singh, and C. Guestrin. Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies. JMLR, 9:235-284, 2008. (Pubitemid 351469021)
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 235-284
    • Krause, A.1    Singh, A.2    Guestrin, C.3
  • 18
    • 0000695404 scopus 로고
    • Information-based objective functions for active data selection
    • D. MacKay. Information-based objective functions for active data selection. Neural Computation, 4:590-604, 1992.
    • (1992) Neural Computation , vol.4 , pp. 590-604
    • MacKay, D.1
  • 21
    • 2142727946 scopus 로고    scopus 로고
    • Limitations of co-training for natural language learning from large datasets
    • D. Pierce and C. Cardie. Limitations of co-training for natural language learning from large datasets. In EMNLP-2001, 2001.
    • (2001) EMNLP-2001
    • Pierce, D.1    Cardie, C.2
  • 23
    • 56449131204 scopus 로고    scopus 로고
    • An RKHS for multi-view learning and manifold coregularization
    • V. Sindhwani and D. S. Rosenberg. An RKHS for multi-view learning and manifold coregularization. In ICML, 2008.
    • (2008) ICML
    • Sindhwani, V.1    Rosenberg, D.S.2
  • 25
    • 84898075155 scopus 로고    scopus 로고
    • An information theoretic framework for multi-view learning
    • K. Sridharan and S. M. Kakade. An information theoretic framework for multi-view learning. In COLT, 2008.
    • (2008) COLT
    • Sridharan, K.1    Kakade, S.M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.