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Volumn , Issue , 2004, Pages 473-480

Gradient LASSO for feature selection

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER PROGRAMMING LANGUAGES; FUNCTIONS; LEARNING SYSTEMS; MATHEMATICAL MODELS; NONLINEAR PROGRAMMING; PROBLEM SOLVING;

EID: 14344256001     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (51)

References (19)
  • 2
    • 0027599793 scopus 로고
    • A universal approximation bounds for superpositions of a sigmoidal function
    • Barron, A. R. (1993). A universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans. Inform. Theory, 39, 930-945.
    • (1993) IEEE Trans. Inform. Theory , vol.39 , pp. 930-945
    • Barron, A.R.1
  • 5
    • 0031211090 scopus 로고    scopus 로고
    • A decisiontheoretic generalization of on-line learning and an application to boosting
    • Freund, Y., & Schapire, R. (1997). A decisiontheoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119-139.
    • (1997) Journal of Computer and System Sciences , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.2
  • 6
    • 0035470889 scopus 로고    scopus 로고
    • Greedy function approximation : A gradient boosting machine
    • Friedman, J. (2001). Greedy function approximation : a gradient boosting machine. Annals of Statistics, 29, 1189-1232.
    • (2001) Annals of Statistics , vol.29 , pp. 1189-1232
    • Friedman, J.1
  • 7
    • 0034164230 scopus 로고    scopus 로고
    • Additive logistic regression: A statistical view of boosting
    • Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28, 337-374.
    • (2000) Annals of Statistics , vol.28 , pp. 337-374
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 8
    • 0041879258 scopus 로고    scopus 로고
    • Outcomes of the equivalence of adaptive ridge with least absolute shrinkage
    • M. Kearns, S. Solla and D. Cohn (Eds.), MIT press
    • Grandvalet, Y., & Canu, S. (1999). Outcomes of the equivalence of adaptive ridge with least absolute shrinkage. In M. Kearns, S. Solla and D. Cohn (Eds.), Advances in neural information processing systems, vol. 11, 445-451. MIT press.
    • (1999) Advances in Neural Information Processing Systems , vol.11 , pp. 445-451
    • Grandvalet, Y.1    Canu, S.2
  • 9
    • 0036643063 scopus 로고    scopus 로고
    • Structural modelling with sparse kernels
    • Gunn, S. R., & Kandola, J. S. (2002). Structural modelling with sparse kernels. Machine Learning, 48, 115-136.
    • (2002) Machine Learning , vol.48 , pp. 115-136
    • Gunn, S.R.1    Kandola, J.S.2
  • 10
    • 0000796112 scopus 로고
    • A simple lemma on greedy approximation in Hubert space and convergence rates for projection pursuit regression and neural network training
    • Jones, L. K. (1992). A simple lemma on greedy approximation in Hubert space and convergence rates for projection pursuit regression and neural network training. Annals of Statistics, 20, 608-613.
    • (1992) Annals of Statistics , vol.20 , pp. 608-613
    • Jones, L.K.1
  • 11
    • 0015000439 scopus 로고
    • Some results on Tchebychffian spline functions
    • Kimeldorf, G., & Wahba, G. (1972). Some results on Tchebychffian spline functions, J. Math. Anal. Applic., 33, 82-95.
    • (1972) J. Math. Anal. Applic. , vol.33 , pp. 82-95
    • Kimeldorf, G.1    Wahba, G.2
  • 12
    • 0034287156 scopus 로고    scopus 로고
    • Asymptotics for lassotype estimators
    • Knight, K., & Fu, W. J. (2000). Asymptotics for lassotype estimators. Annals of Statistics, 28, 1356-1378.
    • (2000) Annals of Statistics , vol.28 , pp. 1356-1378
    • Knight, K.1    Fu, W.J.2
  • 14
    • 9444269961 scopus 로고    scopus 로고
    • On the bayes risk consistency of regularized boosting methods
    • Lugosi, G., & Vayatis, N. (2004). On the bayes risk consistency of regularized boosting methods. Annals of Statistics.
    • (2004) Annals of Statistics
    • Lugosi, G.1    Vayatis, N.2
  • 15
    • 0002550596 scopus 로고    scopus 로고
    • Functional gradient techniques for combining hypotheses
    • A. J. Smola, P. L. Bartlett, B. Scholkopf and D. Schuurmans (Eds.), Cambridge: MIT press
    • Mason, L., Baxter, L., Bartlett, P., & Frean, M. (2000). Functional gradient techniques for combining hypotheses. In A. J. Smola, P. L. Bartlett, B. Scholkopf and D. Schuurmans (Eds.), Advances in large margin classifiers. Cambridge: MIT press.
    • (2000) Advances in Large Margin Classifiers
    • Mason, L.1    Baxter, L.2    Bartlett, P.3    Frean, M.4
  • 17
    • 1942418470 scopus 로고    scopus 로고
    • Grafting: Fast, incremetal feature selection by gradient descent in function space
    • Perkins, S., Lacker, K., & Theiler, J. (2003). Grafting: Fast, incremetal feature selection by gradient descent in function space. Journal of Machine Learnign Research, 3, 1333-1356.
    • (2003) Journal of Machine Learnign Research , vol.3 , pp. 1333-1356
    • Perkins, S.1    Lacker, K.2    Theiler, J.3
  • 18
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J.R. Statist. Soc. (B), 58, 267-288.
    • (1996) J.R. Statist. Soc. (B) , vol.58 , pp. 267-288
    • Tibshirani, R.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.