메뉴 건너뛰기




Volumn 1, Issue , 2012, Pages 323-331

Selective labeling via error bound minimization

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATASETS; COMBINATIONAL PROBLEMS; LEARNING PERFORMANCE; MACHINE LEARNING PROBLEM; OUT-OF-SAMPLE ERRORS; REGULARIZED LEAST SQUARES; SELECTIVE LABELING; STATE-OF-THE-ART METHODS;

EID: 84877789621     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (27)

References (22)
  • 3
    • 0038453192 scopus 로고    scopus 로고
    • Rademacher and Gaussian complexities: Risk bounds and structural results
    • P. L. Bartlett and S. Mendelson. Rademacher and gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3:463-482, 2002.
    • (2002) Journal of Machine Learning Research , vol.3 , pp. 463-482
    • Bartlett, P.L.1    Mendelson, S.2
  • 4
    • 33750729556 scopus 로고    scopus 로고
    • Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
    • M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 2399-2434
    • Belkin, M.1    Niyogi, P.2    Sindhwani, V.3
  • 5
    • 85161966389 scopus 로고    scopus 로고
    • Agnostic active learning without constraints
    • A. Beygelzimer, D. Hsu, J. Langford, and T. Zhang. Agnostic active learning without constraints. In NIPS, pages 199-207, 2010.
    • (2010) NIPS , pp. 199-207
    • Beygelzimer, A.1    Hsu, D.2    Langford, J.3    Zhang, T.4
  • 8
    • 0003882879 scopus 로고    scopus 로고
    • American Mathematical Society, February
    • F. R. K. Chung. Spectral Graph Theory. American Mathematical Society, February 1997.
    • (1997) Spectral Graph Theory
    • Chung, F.R.K.1
  • 9
    • 0028424239 scopus 로고
    • Improving generalization with active learning
    • D. A. Cohn, L. E. Atlas, and R. E. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201-221, 1994.
    • (1994) Machine Learning , vol.15 , Issue.2 , pp. 201-221
    • Cohn, D.A.1    Atlas, L.E.2    Ladner, R.E.3
  • 10
    • 85153947869 scopus 로고
    • Active learning with statistical models
    • D. A. Cohn, Z. Ghahramani, and M. I. Jordan. Active learning with statistical models. In NIPS, pages 705-712, 1994.
    • (1994) NIPS , pp. 705-712
    • Cohn, D.A.1    Ghahramani, Z.2    Jordan, M.I.3
  • 11
    • 0002516752 scopus 로고
    • Spoken letter recognition
    • M. A. Fanty and R. A. Cole. Spoken letter recognition. In NIPS, pages 220-226, 1990.
    • (1990) NIPS , pp. 220-226
    • Fanty, M.A.1    Cole, R.A.2
  • 12
    • 0004236492 scopus 로고    scopus 로고
    • (3rd ed.). Johns Hopkins University Press, Baltimore, MD, USA
    • G. H. Golub and C. F. V. Loan. Matrix computations (3rd ed.). Johns Hopkins University Press, Baltimore, MD, USA, 1996.
    • (1996) Matrix Computations
    • Golub, G.H.1    Loan, V.C.F.2
  • 13
    • 80053162015 scopus 로고    scopus 로고
    • Active semi-supervised learning using submodular functions
    • A. Guillory and J. Bilmes. Active semi-supervised learning using submodular functions. In UAI, pages 274-282, 2011.
    • (2011) UAI , pp. 274-282
    • Guillory, A.1    Bilmes, J.2
  • 14
    • 79551594780 scopus 로고    scopus 로고
    • Rates of convergence in active learning
    • S. Hanneke. Rates of convergence in active learning. The Annals of Statistics, 39(1):333-361, 2011.
    • (2011) The Annals of Statistics , vol.39 , Issue.1 , pp. 333-361
    • Hanneke, S.1
  • 16
    • 36448966739 scopus 로고    scopus 로고
    • Laplacian optimal design for image retrieval
    • X. He, W. Min, D. Cai, and K. Zhou. Laplacian optimal design for image retrieval. In SIGIR, pages 119-126, 2007.
    • (2007) SIGIR , pp. 119-126
    • He, X.1    Min, W.2    Cai, D.3    Zhou, K.4
  • 18
    • 0007696417 scopus 로고    scopus 로고
    • Less is more: Active learning with support vector machines
    • G. Schohn and D. Cohn. Less is more: Active learning with support vector machines. In ICML, pages 839-846, 2000.
    • (2000) ICML , pp. 839-846
    • Schohn, G.1    Cohn, D.2
  • 19
    • 0003007938 scopus 로고    scopus 로고
    • Support vector machine active learning with applications to text classification
    • S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In ICML, pages 999-1006, 2000.
    • (2000) ICML , pp. 999-1006
    • Tong, S.1    Koller, D.2
  • 20
    • 33749265864 scopus 로고    scopus 로고
    • Active learning via transductive experimental design
    • K. Yu, J. Bi, and V. Tresp. Active learning via transductive experimental design. In ICML, pages 1081-1088, 2006.
    • (2006) ICML , pp. 1081-1088
    • Yu, K.1    Bi, J.2    Tresp, V.3
  • 22
    • 1942484430 scopus 로고    scopus 로고
    • Semi-supervised learning using Gaussian fields and harmonic functions
    • X. Zhu, Z. Ghahramani, and J. D. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In ICML, pages 912-919, 2003.
    • (2003) ICML , pp. 912-919
    • Zhu, X.1    Ghahramani, Z.2    Lafferty, J.D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.