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Volumn 39, Issue 10, 2006, Pages 1864-1875

Gaussian fields for semi-supervised regression and correspondence learning

Author keywords

Active learning; Gaussian fields; Model selection; Regression

Indexed keywords

ALGORITHMS; DATA REDUCTION; ENTROPY; LEARNING SYSTEMS; REGRESSION ANALYSIS;

EID: 33745417721     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2006.04.011     Document Type: Article
Times cited : (31)

References (13)
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  • 2
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    • A global geometric framework for nonlinear dimensionality reduction
    • Tenenbaum J.B., de Silva V., and Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 290 5500 (2000) 2319-2323
    • (2000) Science , vol.290 , Issue.5500 , pp. 2319-2323
    • Tenenbaum, J.B.1    de Silva, V.2    Langford, J.C.3
  • 3
    • 0042378381 scopus 로고    scopus 로고
    • Laplacian eigenmaps for dimensionality reduction and data representation
    • Belkin M., and Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15 6 (2003) 1373-1396
    • (2003) Neural Comput. , vol.15 , Issue.6 , pp. 1373-1396
    • Belkin, M.1    Niyogi, P.2
  • 4
    • 1942484960 scopus 로고    scopus 로고
    • Transductive learning via spectral graph partitioning
    • Fawcett T., and Mishra N. (Eds), AAAI Press, New York
    • Joachims T. Transductive learning via spectral graph partitioning. In: Fawcett T., and Mishra N. (Eds). Proceedings of the International Conference on Machine Learning vol. 20 (2003), AAAI Press, New York 290-297
    • (2003) Proceedings of the International Conference on Machine Learning , vol.20 , pp. 290-297
    • Joachims, T.1
  • 6
    • 3142725535 scopus 로고    scopus 로고
    • Semi-supervised learning on Riemannian manifolds
    • Belkin M., and Niyogi P. Semi-supervised learning on Riemannian manifolds. Mach. Learning 56 1-3 (2004) 209-239
    • (2004) Mach. Learning , vol.56 , Issue.1-3 , pp. 209-239
    • Belkin, M.1    Niyogi, P.2
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    • D.J.C. MacKay, Bayesian methods for adaptive models, Ph.D. Thesis, California Institute of Technology, 1991.
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    • Non-linear CCA and PCA by alignment of local models
    • Thrun S., Saul L.K., and Schölkopf B. (Eds), MIT Press, Cambridge, MA, USA
    • Verbeek J.J., Roweis S.T., and Vlassis N. Non-linear CCA and PCA by alignment of local models. In: Thrun S., Saul L.K., and Schölkopf B. (Eds). Advances in Neural Information Processing Systems vol. 16 (2004), MIT Press, Cambridge, MA, USA 297-304
    • (2004) Advances in Neural Information Processing Systems , vol.16 , pp. 297-304
    • Verbeek, J.J.1    Roweis, S.T.2    Vlassis, N.3
  • 13
    • 33745343568 scopus 로고    scopus 로고
    • R.M. Neal, Bayesian Learning for Neural Networks, Ph.D. Thesis, University of Toronto, 1995.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.