메뉴 건너뛰기




Volumn 3, Issue , 2003, Pages 1333-1356

Grafting: Fast, incremental feature selection by gradient descent in function space

Author keywords

Boosting; Feature selection; Functional gradient descent; Loss functions; Margin space

Indexed keywords

BOOSTING; FUNCTIONAL GRADIENT DESCENT; ITERATIVE FASHION; LARGE-SCALE PROBLEM; LEARNING FRAMEWORKS; LOSS FUNCTIONS; MARGIN SPACE; NON-LINEAR MODEL;

EID: 1942418470     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (269)

References (23)
  • 1
    • 0003408496 scopus 로고    scopus 로고
    • University of California, Irvine, Dept. of Information and Computer Science
    • C.L. Blake and C.J. Merz. UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html, 1998. University of California, Irvine, Dept. of Information and Computer Science.
    • (1998) UCI Repository of Machine Learning Databases
    • Blake, C.L.1    Merz, C.J.2
  • 3
    • 10044235999 scopus 로고    scopus 로고
    • LIBSVM: A library for support vector machines
    • C. Chang and C. Lin. LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001.
    • (2001) Software
    • Chang, C.1    Lin, C.2
  • 4
    • 0010739663 scopus 로고    scopus 로고
    • Filters, wrappers and a boosting-based hybrid for feature selection
    • Morgan Kauffmann
    • S. Das. Filters, wrappers and a boosting-based hybrid for feature selection. In Proc. ICML. Morgan Kauffmann, 2001.
    • (2001) Proc. ICML.
    • Das, S.1
  • 6
    • 0034164230 scopus 로고    scopus 로고
    • Additive logistic regression: A statistical view of boosting
    • J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28:337-307, 2000.
    • (2000) Annals of Statistics , vol.28 , pp. 337-307
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 9
    • 0001219859 scopus 로고
    • Regularization theory and neural networks architectures
    • F. Girosi, M. Jones, and T. Poggio. Regularization theory and neural networks architectures. Neural Computation, 7:219-269, 1995.
    • (1995) Neural Computation , vol.7 , pp. 219-269
    • Girosi, F.1    Jones, M.2    Poggio, T.3
  • 11
    • 84942484786 scopus 로고
    • Ridge regression: Biased estimation for nonorthogonal problems
    • A.E. Hoerl and R. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12:55-67, 1970.
    • (1970) Technometrics , vol.12 , pp. 55-67
    • Hoerl, A.E.1    Kennard, R.2
  • 13
    • 85146422424 scopus 로고
    • A practical approach to feature selection
    • D. Sleeman and P. Edwards, editors. Morgan Kaufmann
    • K. Kira and L. Rendell. A practical approach to feature selection. In D. Sleeman and P. Edwards, editors, Proc. Int. Conf. on Machine Learning, pages 249-256. Morgan Kaufmann, 1992.
    • (1992) Proc. Int. Conf. on Machine Learning , pp. 249-256
    • Kira, K.1    Rendell, L.2
  • 14
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi and G.H. John. Wrappers for feature subset selection. Artificial Intelligence, 97:273-324, 1997.
    • (1997) Artificial Intelligence , vol.97 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 15
    • 0027842081 scopus 로고
    • Matching pursuit with time-frequency dictionaries
    • S. Mallat and Z. Zhang. Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41(12):3397-3415, 1993.
    • (1993) IEEE Transactions on Signal Processing , vol.41 , Issue.12 , pp. 3397-3415
    • Mallat, S.1    Zhang, Z.2
  • 16
    • 0002550596 scopus 로고    scopus 로고
    • Functional gradient techniques for combining hypotheses
    • A.J. Smola, P.L Bartlett, B. Scölkopf, and D. Schuurmans, editors, MIT Press
    • L. Mason, J. Baxter, P.L. Bartlett, and M. Frean. Functional gradient techniques for combining hypotheses. In A.J. Smola, P.L Bartlett, B. Scölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 221-246. MIT Press, 2000.
    • (2000) Advances in Large Margin Classifiers , pp. 221-246
    • Mason, L.1    Baxter, J.2    Bartlett, P.L.3    Frean, M.4
  • 17
    • 0000238336 scopus 로고
    • A simplex method for function minimization
    • J.A. Nelder and R. Mead. A simplex method for function minimization. Computer Journal, 7: 308-313, 1965.
    • (1965) Computer Journal , vol.7 , pp. 308-313
    • Nelder, J.A.1    Mead, R.2
  • 19
    • 0033281701 scopus 로고    scopus 로고
    • Improved boosting algorithms using confidence-rated predictions
    • R.E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37(3):297-336, 1999.
    • (1999) Machine Learning , vol.37 , Issue.3 , pp. 297-336
    • Schapire, R.E.1    Singer, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.