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Volumn 7, Issue , 2006, Pages 519-549

Learning coordinate covariances via gradients

Author keywords

Generalization bounds; Reproducing kernel Hilbert space; Tikhnonov regularization; Variable selection

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DATA PROCESSING; DATA STORAGE EQUIPMENT; ERROR ANALYSIS; GRADIENT METHODS; PROBLEM SOLVING; SET THEORY; TIME AND MOTION STUDY;

EID: 33646374652     PISSN: 15337928     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (87)

References (34)
  • 2
    • 3142725535 scopus 로고    scopus 로고
    • Semi-supervised learning on riemannian manifolds
    • M. Belkin and P. Niyogi. Semi-Supervised Learning on Riemannian Manifolds. Machine Learning, 56(1-3):209-239, 2004.
    • (2004) Machine Learning , vol.56 , Issue.1-3 , pp. 209-239
    • Belkin, M.1    Niyogi, P.2
  • 3
    • 0033435207 scopus 로고    scopus 로고
    • A first-generation X-incativation profile of the human X chromosome
    • I. Carrel, A. Cottle, K. Coglin, and H. Willard. A first-generation X-incativation profile of the human X chromosome. Proc. Natl. Acad. Sci. USA, 96:14440-14444, 1999.
    • (1999) Proc. Natl. Acad. Sci. USA , vol.96 , pp. 14440-14444
    • Carrel, I.1    Cottle, A.2    Coglin, K.3    Willard, H.4
  • 4
    • 0036161011 scopus 로고    scopus 로고
    • Choosing multiple parameters for support vector machines
    • O. Chapelle, V. N. Vapnik, O. Bousquet, and S. Mukherjee. Choosing Multiple Parameters for Support Vector Machines. Machine Learning, 46(1-3):131-159, 2002.
    • (2002) Machine Learning , vol.46 , Issue.1-3 , pp. 131-159
    • Chapelle, O.1    Vapnik, V.N.2    Bousquet, O.3    Mukherjee, S.4
  • 6
    • 34249753618 scopus 로고
    • Support-vector networks
    • C. Cortes and V. N. Vapnik. Support-Vector Networks. Machine Learning, 20(3):273-297, 1995.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 273-297
    • Cortes, C.1    Vapnik, V.N.2
  • 7
    • 0036071370 scopus 로고    scopus 로고
    • On the mathematical foundations of learning
    • F. Cucker and S. Smale. On the mathematical foundations of learning. Bull. Amer. Math. Soc., 39: 1-49, 2001.
    • (2001) Bull. Amer. Math. Soc. , vol.39 , pp. 1-49
    • Cucker, F.1    Smale, S.2
  • 11
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine Learning, 46(1-3):389-422, 2002.
    • (2002) Machine Learning , vol.46 , Issue.1-3 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3    Vapnik, V.4
  • 12
    • 2142775432 scopus 로고    scopus 로고
    • Multicategory support vector machines: Theory and applications to the classification of microarray data and satellite radiance data
    • Y. Lee, Y. Lin, and G. Wahba. Multicategory support vector machines: theory and applications to the classification of microarray data and satellite radiance data. Journal of the American Statistical Society, 99:67-81, 2004.
    • (2004) Journal of the American Statistical Society , vol.99 , pp. 67-81
    • Lee, Y.1    Lin, Y.2    Wahba, G.3
  • 15
    • 14544299611 scopus 로고    scopus 로고
    • On learning vector-valued functions
    • C. A. Micchelli and M. Pontil. On learning vector-valued functions. Neural Computation, 17: 177-204, 2005.
    • (2005) Neural Computation , vol.17 , pp. 177-204
    • Micchelli, C.A.1    Pontil, M.2
  • 16
    • 0001638327 scopus 로고
    • Optimum bounds for the distributions of martingales in Banach spaces
    • I. Pinelis. Optimum bounds for the distributions of martingales in Banach spaces. Ann. Probab., 22:1679-1706, 1994.
    • (1994) Ann. Probab. , vol.22 , pp. 1679-1706
    • Pinelis, I.1
  • 17
    • 33646361736 scopus 로고    scopus 로고
    • Correction: "Optimum bounds for the distributions of martingales in Banach spaces"
    • I. Pinelis. Correction: "Optimum bounds for the distributions of martingales in Banach spaces". Ann. Probab., 27:2119, 1999.
    • (1999) Ann. Probab. , vol.27 , pp. 2119
    • Pinelis, I.1
  • 18
    • 0025056697 scopus 로고
    • Regularization algorithms for learning that are equivalent to multilayer networks
    • T. Poggio and F. Girosi. Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247:978-982, 1990.
    • (1990) Science , vol.247 , pp. 978-982
    • Poggio, T.1    Girosi, F.2
  • 21
    • 33646361487 scopus 로고    scopus 로고
    • Learning theory estimates via integral operators and their approximations
    • S. Smale and D. X. Zhou. Learning theory estimates via integral operators and their approximations. Constr. Approx., 24, 2006a.
    • (2006) Constr. Approx. , vol.24
    • Smale, S.1    Zhou, D.X.2
  • 22
    • 27844555491 scopus 로고    scopus 로고
    • Shannon sampling II. Connections to learning theory
    • S. Smale and D. X. Zhou. Shannon sampling II. Connections to learning theory. Appl. Comput. Harmonic Anal., 19:285-302, 2006b.
    • (2006) Appl. Comput. Harmonic Anal. , vol.19 , pp. 285-302
    • Smale, S.1    Zhou, D.X.2
  • 23
    • 3042850649 scopus 로고    scopus 로고
    • Shannon sampling and function reconstruction from point values
    • S. Smale and D. X. Zhou. Shannon sampling and function reconstruction from point values. Bull. Amer. Math. Soc., 41:279-305, 2004.
    • (2004) Bull. Amer. Math. Soc. , vol.41 , pp. 279-305
    • Smale, S.1    Zhou, D.X.2
  • 24
    • 0037749769 scopus 로고    scopus 로고
    • Estimating the approximation error in learning theory
    • S. Smale and D. X. Zhou. Estimating the approximation error in learning theory. Anal. Appl., 1: 17-41, 2003.
    • (2003) Anal. Appl. , vol.1 , pp. 17-41
    • Smale, S.1    Zhou, D.X.2
  • 27
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • R. Tibshirani. Regression shrinkage and selection via the lasso. J Royal Stat Soc B, 58(1):267-288, 1996.
    • (1996) J Royal Stat Soc B , vol.58 , Issue.1 , pp. 267-288
    • Tibshirani, R.1
  • 29
    • 24944432318 scopus 로고    scopus 로고
    • Model selection for regularized least-squares algorithm in learning
    • E. De Vito, A. Caponnetto, and L. Rosasco. Model selection for regularized least-squares algorithm in learning. Foundat. Comput. Math., 5:59-85, 2005.
    • (2005) Foundat. Comput. Math. , vol.5 , pp. 59-85
    • De Vito, E.1    Caponnetto, A.2    Rosasco, L.3
  • 30
    • 0000681041 scopus 로고
    • Some new mathematical methods for variational objective analysis using splines and cross-validation
    • G. Wahba and J. Wendelberger. Some new mathematical methods for variational objective analysis using splines and cross-validation. Monthly Weather Rev., 108:1122-1145, 1980.
    • (1980) Monthly Weather Rev. , vol.108 , pp. 1122-1145
    • Wahba, G.1    Wendelberger, J.2
  • 31
    • 0242295767 scopus 로고    scopus 로고
    • Bayesian factor regression models in the "large p, small n" paradigm
    • J. M. Bernardo et al., editor, Oxford
    • M. West. Bayesian factor regression models in the "large p, small n" paradigm. In J. M. Bernardo et al., editor, Bayesian Statistics 7, pages 723-732. Oxford, 2003.
    • (2003) Bayesian Statistics , vol.7 , pp. 723-732
    • West, M.1
  • 32
    • 17444402055 scopus 로고    scopus 로고
    • Support vector machine classifiers: Linear programming versus quadratic programming
    • Q. Wu and D. X. Zhou. Support vector machine classifiers: linear programming versus quadratic programming. Neural Computation, 17:1160-1187, 2005.
    • (2005) Neural Computation , vol.17 , pp. 1160-1187
    • Wu, Q.1    Zhou, D.X.2
  • 33
    • 0042879446 scopus 로고    scopus 로고
    • Leave-one-out bounds for kernel methods
    • T. Zhang. Leave-one-out bounds for kernel methods. Neural Computation, 15(6): 1397-1437, 2003.
    • (2003) Neural Computation , vol.15 , Issue.6 , pp. 1397-1437
    • Zhang, T.1
  • 34
    • 0038105204 scopus 로고    scopus 로고
    • Capacity of reproducing kernel spaces in learning theory
    • D. X. Zhou. Capacity of reproducing kernel spaces in learning theory. IEEE Trans. Inform. Theory, 49:1743-1752, 2003.
    • (2003) IEEE Trans. Inform. Theory , vol.49 , pp. 1743-1752
    • Zhou, D.X.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.