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Volumn 13, Issue , 2012, Pages 671-690

Structured sparsity and generalization

Author keywords

Empirical processes; Rademacher average; Sparse estimation

Indexed keywords

ARTIFICIAL INTELLIGENCE;

EID: 84859471498     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (51)

References (27)
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    • Elastic-net regularization in learning theory
    • C. De Mol, E. De Vito, L. Rosasco. Elastic-net regularization in learning theory. Journal of Complexity, 25(2):201-230, 2009.
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    • De Mol, C.1    De Vito, E.2    Rosasco, L.3
  • 7
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    • 0036104545 scopus 로고    scopus 로고
    • Empirical margin distributions and bounding the generalization error of combined classifiers
    • V. Koltchinskii and D. Panchenko, Empirical margin distributions and bounding the generalization error of combined classifiers, Annals of Statistics, 30(1): 1-50, 2002. (Pubitemid 37095367)
    • (2002) Annals of Statistics , vol.30 , Issue.1 , pp. 1-50
    • Koltchinskii, V.1    Panchenko, D.2
  • 16
    • 84855412474 scopus 로고    scopus 로고
    • Oracle inequalities and optimal inference under group sparsity
    • K. Lounici, M. Pontil, A.B. Tsybakov and S. van de Geer. Oracle inequalities and optimal inference under group sparsity. Annals of Statistics, 39(4):2164-2204, 2011.
    • (2011) Annals of Statistics , vol.39 , Issue.4 , pp. 2164-2204
    • Lounici, K.1    Pontil, M.2    Tsybakov, A.B.3    Geer De S.Van4
  • 19
    • 3142691501 scopus 로고    scopus 로고
    • Generalization error bounds for Bayesian mixture algorithms
    • R. Meir and T. Zhang. Generalization error bounds for Bayesian mixture algorithms. Journal of Machine Learning Research, 4:839-860, 2003.
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    • Meir, R.1    Zhang, T.2
  • 22
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    • Feature space perspectives for learning the kernel
    • DOI 10.1007/s10994-006-0679-0, Special Issue on Learning Theory
    • C.A. Micchelli and M. Pontil. Feature space perspectives for learning the kernel. Machine Learning, 66:297-319,2007. (Pubitemid 46360616)
    • (2007) Machine Learning , vol.66 , Issue.2-3 , pp. 297-319
    • Micchelli, C.A.1    Pontil, M.2
  • 24
    • 46749151407 scopus 로고    scopus 로고
    • Weighted Lasso in graphical Gaussian modeling for large gene network estimation based on microarray data
    • T. Shimamura, S. Imoto, R. Yamaguchi and S. Miyano. Weighted Lasso in graphical Gaussian modeling for large gene network estimation based on microarray data. Genome Informatics, 19:142-153, 2007.
    • (2007) Genome Informatics , vol.19 , pp. 142-153
    • Shimamura, T.1    Imoto, S.2    Yamaguchi, R.3    Miyano, S.4
  • 26
  • 27
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    • Grouped and hierarchical model selection through composite absolute penalties
    • P. Zhao and G. Rocha and B. Yu. Grouped and hierarchical model selection through composite absolute penalties. Annals of Statistics, 37(6A):3468-3497, 2009.
    • (2009) Annals of Statistics , vol.37 , Issue.6 A , pp. 3468-3497
    • Zhao, P.1    Rocha, G.2    Yu, B.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.