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Volumn 3720 LNAI, Issue , 2005, Pages 146-157

Kernel basis pursuit

Author keywords

LASSO; Multiple Kernels; Parameter Free; Regression

Indexed keywords

LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR (LASSO); MULTIPLE KERNELS; PARAMETER FREE; REGRESSIONS;

EID: 33646395927     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564096_18     Document Type: Conference Paper
Times cited : (20)

References (20)
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  • 3
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    • Wahba, G.1
  • 4
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    • Some results on Tchebycheffian spline functions
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    • (1971) J. Math. Anal. Applic. , vol.33 , pp. 82-95
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  • 5
    • 85194972808 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal. Statist. 58 (1996) 267-288
    • (1996) J. Royal. Statist. , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 12
    • 0003958737 scopus 로고
    • PhD thesis, Department of Statistics, Stanford University
    • Chen, S.: Basis Pursuit. PhD thesis, Department of Statistics, Stanford University (1995)
    • (1995) Basis Pursuit
    • Chen, S.1
  • 13
    • 0004123838 scopus 로고    scopus 로고
    • Least absolute shrinkage is equivalent to quadratic penalization
    • Grandvalet, Y.: Least absolute shrinkage is equivalent to quadratic penalization. In: ICANN. (1998) 201-206
    • (1998) ICANN , pp. 201-206
    • Grandvalet, Y.1
  • 18
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    • Ideal spatial adaptation by wavelet shrinkage
    • Donoho, D., Johnstone, I.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81 (1994) 425-455
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    • Donoho, D.1    Johnstone, I.2
  • 19
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    • Leave-one-out bounds for support vector regression model selection
    • Chang, M., Lin, C.: Leave-one-out bounds for support vector regression model selection. Neural Computation (2005)
    • (2005) Neural Computation
    • Chang, M.1    Lin, C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.