메뉴 건너뛰기




Volumn 2005-September, Issue , 2005, Pages

Semi-supervised face detection

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); COMPUTER VISION; SEMI-SUPERVISED LEARNING;

EID: 85040145691     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2005.523     Document Type: Conference Paper
Times cited : (2)

References (31)
  • 1
    • 0141849712 scopus 로고    scopus 로고
    • MIT Center For Biological and Computation Learning
    • CBCL Face Database #1. MIT Center For Biological and Computation Learning.
    • CBCL Face Database #1
  • 2
    • 84898963451 scopus 로고    scopus 로고
    • Probabilistic modeling for face orientation discrimination: Learning from labeled and unlabeled data
    • S. Baluja. Probabilistic modeling for face orientation discrimination: Learning from labeled and unlabeled data. In NIPS, pages 854-860, 1998.
    • (1998) NIPS , pp. 854-860
    • Baluja, S.1
  • 4
    • 0036567524 scopus 로고    scopus 로고
    • Learning Bayesian networks from data: An information-theory based approach
    • J. Cheng, R. Greiner, J. Kelly, D. Bell, and W. Liu. Learning Bayesian networks from data: An information-theory based approach. The Artificial Intell. Journal, 137:43-90, 2002.
    • (2002) The Artificial Intell. Journal , vol.137 , pp. 43-90
    • Cheng, J.1    Greiner, R.2    Kelly, J.3    Bell, D.4    Liu, W.5
  • 5
    • 9244243116 scopus 로고    scopus 로고
    • Semi-supervised learning of classifiers: Theory, algorithms, and their applications to human-computer interaction
    • I. Cohen, F. Cozman, N. Sebe, M. Cirello, and T.S. Huang. Semi-supervised learning of classifiers: Theory, algorithms, and their applications to human-computer interaction. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26(12):1553-1567, 2004.
    • (2004) IEEE Trans. On Pattern Analysis and Machine Intelligence , vol.26 , Issue.12 , pp. 1553-1567
    • Cohen, I.1    Cozman, F.2    Sebe, N.3    Cirello, M.4    Huang, T.S.5
  • 6
    • 0030655218 scopus 로고    scopus 로고
    • Face detection with information based maximum discrimination
    • A.J. Colmenarez and T.S. Huang. Face detection with information based maximum discrimination. In CVPR, pages 782-787, 1997.
    • (1997) CVPR , pp. 782-787
    • Colmenarez, A.J.1    Huang, T.S.2
  • 7
    • 0347264974 scopus 로고    scopus 로고
    • Continuations methods for mixing heterogeneous sources
    • A. Corduneanu and T. Jaakkola. Continuations methods for mixing heterogeneous sources. In UAI, pages 111-118, 2002.
    • (2002) UAI , pp. 111-118
    • Corduneanu, A.1    Jaakkola, T.2
  • 10
    • 85139983802 scopus 로고
    • Supervised and unsupervised discretization of continuous features
    • J. Dougherty, R. Kohavi, and M. Sahami. Supervised and unsupervised discretization of continuous features. In ICML, pages 194-202, 1995.
    • (1995) ICML , pp. 194-202
    • Dougherty, J.1    Kohavi, R.2    Sahami, M.3
  • 11
    • 21744462998 scopus 로고    scopus 로고
    • On bias, variance, 0/1-loss, and the curse-of-dimensionality
    • J.H. Friedman. On bias, variance, 0/1-loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery, 1(1):55-77, 1997.
    • (1997) Data Mining and Knowledge Discovery , vol.1 , Issue.1 , pp. 55-77
    • Friedman, J.H.1
  • 12
    • 0000854197 scopus 로고    scopus 로고
    • The Bayesian structural EM algorithm
    • N. Friedman. The Bayesian structural EM algorithm. In UAI, pages 129-138, 1998.
    • (1998) UAI , pp. 129-138
    • Friedman, N.1
  • 14
    • 14344263553 scopus 로고    scopus 로고
    • Combining labeled and unlabeled data for multiclass text categorization
    • R. Ghani. Combining labeled and unlabeled data for multiclass text categorization. In ICML, pages 187-194, 2002.
    • (2002) ICML , pp. 187-194
    • Ghani, R.1
  • 15
    • 0036927090 scopus 로고    scopus 로고
    • Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers
    • R. Greiner and W. Zhou. Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers. In UAI, pages 167-173, 2002.
    • (2002) UAI , pp. 167-173
    • Greiner, R.1    Zhou, W.2
  • 19
    • 0002332781 scopus 로고    scopus 로고
    • Employing EM and pool-based active learning for text classification
    • A.K. McCallum and K. Nigam. Employing EM and pool-based active learning for text classification. In ICML, pages 359-367, 1998.
    • (1998) ICML , pp. 359-367
    • McCallum, A.K.1    Nigam, K.2
  • 20
    • 0033886806 scopus 로고    scopus 로고
    • Text classification from labeled and unlabeled documents using EM
    • K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2-3):103-134, 2000.
    • (2000) Machine Learning , vol.39 , Issue.2-3 , pp. 103-134
    • Nigam, K.1    McCallum, A.2    Thrun, S.3    Mitchell, T.4
  • 21
    • 0000734588 scopus 로고
    • Normal discrimination with unclassified obseravations
    • T.J. O'Neill. Normal discrimination with unclassified obseravations. Journal of the American Statistical Association, 73(364):821-826, 1978.
    • (1978) Journal of the American Statistical Association , vol.73 , Issue.364 , pp. 821-826
    • O'Neill, T.J.1
  • 22
    • 0030673582 scopus 로고    scopus 로고
    • Training support vector machines: An application to face detection
    • E. Osuna, R. Freund, and F. Girosi. Training support vector machines: An application to face detection. In CVPR, pages 130-136, 1997.
    • (1997) CVPR , pp. 130-136
    • Osuna, E.1    Freund, R.2    Girosi, F.3
  • 24
    • 5044231638 scopus 로고    scopus 로고
    • Learning a restricted Bayesian network for object detection
    • H. Schneiderman. Learning a restricted Bayesian network for object detection. In CVPR, pages 639-646, 2004.
    • (2004) CVPR , pp. 639-646
    • Schneiderman, H.1
  • 25
    • 0005977840 scopus 로고    scopus 로고
    • Learning with labeled and unlabeled data
    • Univ. of Edinburgh
    • M. Seeger. Learning with labeled and unlabeled data. In TR., Univ. of Edinburgh, 2002.
    • (2002) TR
    • Seeger, M.1
  • 26
    • 0028499630 scopus 로고
    • Effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon
    • B. Shahshahani and D. Landgrebe. Effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. IEEE Trans. Geoscience and Remote Sensing, 32(5):1087-1095, 1994.
    • (1994) IEEE Trans. Geoscience and Remote Sensing , vol.32 , Issue.5 , pp. 1087-1095
    • Shahshahani, B.1    Landgrebe, D.2
  • 27
    • 2142812371 scopus 로고    scopus 로고
    • Robust real-time object detection
    • P. Viola and M. Jones. Robust real-time object detection. IJCV, 57(2):137-154, 2004.
    • (2004) IJCV , vol.57 , Issue.2 , pp. 137-154
    • Viola, P.1    Jones, M.2
  • 30
    • 0000320045 scopus 로고    scopus 로고
    • A SNoW based face detector
    • M.-H. Yang, D. Roth, and N. Ahuja. A SNoW based face detector. In NIPS, pages 855-861, 2000.
    • (2000) NIPS , pp. 855-861
    • Yang, M.-H.1    Roth, D.2    Ahuja, N.3
  • 31
    • 0005004572 scopus 로고    scopus 로고
    • A probability analysis on the value of unlabeled data for classification problems
    • T. Zhang and F. Oles. A probability analysis on the value of unlabeled data for classification problems. In ICML, pages 1191-1198, 2000.
    • (2000) ICML , pp. 1191-1198
    • Zhang, T.1    Oles, F.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.