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Volumn 4005 LNAI, Issue , 2006, Pages 154-168

Mercer's theorem, feature maps, and smoothing

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION; LEARNING ALGORITHMS; MAPS;

EID: 33746047655     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11776420_14     Document Type: Conference Paper
Times cited : (174)

References (14)
  • 2
    • 31844446899 scopus 로고    scopus 로고
    • Manifold regularization: A geometric framework for learning from examples
    • accepted for publication
    • M. Belkin, P. Niyogi, and V. Sindwani. Manifold Regularization: a Geometric Framework for Learning from Examples. University of Chicago Computer Science Technical Report TR-2004-06, 2004, accepted for publication.
    • (2004) University of Chicago Computer Science Technical Report , vol.TR-2004-06
    • Belkin, M.1    Niyogi, P.2    Sindwani, V.3
  • 3
    • 3142725535 scopus 로고    scopus 로고
    • Semi-supervised learning on riemannian manifolds
    • Special Issue on Clustering
    • M. Belkin and P. Niyogi. Semi-supervised Learning on Riemannian Manifolds. Machine Learning, Special Issue on Clustering, vol. 56, pages 209-239, 2004.
    • (2004) Machine Learning , vol.56 , pp. 209-239
    • Belkin, M.1    Niyogi, P.2
  • 4
    • 24944432318 scopus 로고    scopus 로고
    • Model selection for regularized LeastSquares algorithm in learning theory
    • E. De Vito, A. Caponnetto, and L. Rosasco. Model Selection for Regularized LeastSquares Algorithm in Learning Theory. Foundations of Computational Mathematics, vol. 5, no. 1, pages 59-85, 2005.
    • (2005) Foundations of Computational Mathematics , vol.5 , Issue.1 , pp. 59-85
    • De Vito, E.1    Caponnetto, A.2    Rosasco, L.3
  • 10
    • 33746072423 scopus 로고    scopus 로고
    • An explicit description of the reproducing kernel hubert spaces of gaussian RBF kernels kernels
    • December
    • I. Steinwart, D. Hush, and C. Scovel. An Explicit Description of the Reproducing Kernel Hubert Spaces of Gaussian RBF Kernels Kernels. Los Alamos National Laboratory Technical Report LA-UR-04-8274, December 2005.
    • (2005) Los Alamos National Laboratory Technical Report , vol.LA-UR-04-8274
    • Steinwart, I.1    Hush, D.2    Scovel, C.3
  • 12
    • 33746099532 scopus 로고
    • CBMS-NSF Regional Conference Series in Applied Mathematics 59, Society for Industrial and Applied Mathematics, Philadelphia
    • G. Wahba. Spline Models for Observational Data. CBMS-NSF Regional Conference Series in Applied Mathematics 59, Society for Industrial and Applied Mathematics, Philadelphia, 1990.
    • (1990) Spline Models for Observational Data
    • Wahba, G.1
  • 14
    • 0036748375 scopus 로고    scopus 로고
    • The covering number in learning theory
    • D.X. Zhou. The Covering Number in Learning Theory. Journal of Complexity, vol. 18, pages 739-767, 2002.
    • (2002) Journal of Complexity , vol.18 , pp. 739-767
    • Zhou, D.X.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.