메뉴 건너뛰기




Volumn 22, Issue 12, 2010, Pages 3221-3235

Least square regression with lp-coefficient regularization

Author keywords

[No Author keywords available]

Indexed keywords


EID: 78649659487     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00044     Document Type: Letter
Times cited : (22)

References (26)
  • 1
    • 5844297152 scopus 로고
    • Theory of reproducing kernels
    • Aronszajn, N. (1950). Theory of reproducing kernels. Trans. Amer. Math. Soc., 68, 337-404.
    • (1950) Trans. Amer. Math. Soc. , vol.68 , pp. 337-404
    • Aronszajn, N.1
  • 2
    • 0032028728 scopus 로고    scopus 로고
    • The sample complexity of pattern classification with neural networks: The size of the weights is more import than the size of the network
    • Bartlett, P. L. (1998). The sample complexity of pattern classification with neural networks: The size of the weights is more import than the size of the network. IEEE Trans. Inform. Theory, 44, 525-536.
    • (1998) IEEE Trans. Inform. Theory , vol.44 , pp. 525-536
    • Bartlett, P.L.1
  • 3
    • 0033721433 scopus 로고    scopus 로고
    • Massive data discrimination via linear support vector machines
    • Bradley, P. S., & Mangasarian, O. L. (2000). Massive data discrimination via linear support vector machines. Optimization Methods and Software, 13, 1-10.
    • (2000) Optimization Methods and Software , vol.13 , pp. 1-10
    • Bradley, P.S.1    Mangasarian, O.L.2
  • 4
    • 34548537866 scopus 로고    scopus 로고
    • Optimal rates for regularized least-squares algorithm
    • Caponnetto, A., & De Vito, E. (2007). Optimal rates for regularized least-squares algorithm. Found. Comput. Math., 7, 331-368.
    • (2007) Found. Comput. Math. , vol.7 , pp. 331-368
    • Caponnetto, A.1    De Vito, E.2
  • 5
    • 0036071370 scopus 로고    scopus 로고
    • On the mathematical foundations of learning theory
    • Cucker, F., & Smale, S. (2001). On the mathematical foundations of learning theory. Bull. Amer. Math. Soc., 39, 1-49.
    • (2001) Bull. Amer. Math. Soc. , vol.39 , pp. 1-49
    • Cucker, F.1    Smale, S.2
  • 6
    • 0036436325 scopus 로고    scopus 로고
    • Best choices for regularization parameters in learning theory: On the bias-variance problem
    • Cucker, F., & Smale, S. (2002). Best choices for regularization parameters in learning theory: On the bias-variance problem. Found. Comput. Math., 2, 413-428.
    • (2002) Found. Comput. Math. , vol.2 , pp. 413-428
    • Cucker, F.1    Smale, S.2
  • 7
    • 7044231546 scopus 로고    scopus 로고
    • An iterative thresholding algorithm for linear inverse problems with sparsity constraint
    • Daubechies, I., Defrise, M., & Demol, C. (2004). An iterative thresholding algorithm for linear inverse problems with sparsity constraint. Comm. Pure Appl. Math., 57, 1413-1541.
    • (2004) Comm. Pure Appl. Math. , vol.57 , pp. 1413-1541
    • Daubechies, I.1    Defrise, M.2    Demol, C.3
  • 8
    • 0034419669 scopus 로고    scopus 로고
    • Regularization networks and support vector machines
    • Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Adv. Comput. Math., 13, 1-50.
    • (2000) Adv. Comput. Math. , vol.13 , pp. 1-50
    • Evgeniou, T.1    Pontil, M.2    Poggio, T.3
  • 9
    • 0001219859 scopus 로고
    • Regularization theory and neural network architectures
    • Girosi, F., Jones, M., & Poggio, T. (1995). Regularization theory and neural network architectures. Neural Comput., 7, 219-269.
    • (1995) Neural Comput. , vol.7 , pp. 219-269
    • Girosi, F.1    Jones, M.2    Poggio, T.3
  • 11
  • 12
    • 0000482137 scopus 로고    scopus 로고
    • On the relationship between generalization error, hypothesis complexity, and sample complexity for radical basis functions
    • Niyogi, N., & Girosi, F. (1996). On the relationship between generalization error, hypothesis complexity, and sample complexity for radical basis functions. Neural Comput., 8, 819-842.
    • (1996) Neural Comput. , vol.8 , pp. 819-842
    • Niyogi, N.1    Girosi, F.2
  • 16
    • 0037749769 scopus 로고    scopus 로고
    • Estimating the approximation error in learning theory
    • Smale, S., & Zhou, D. X. (2003). Estimating the approximation error in learning theory. Anal. Appl., 1, 17-41.
    • (2003) Anal. Appl. , vol.1 , pp. 17-41
    • Smale, S.1    Zhou, D.X.2
  • 17
    • 27844555491 scopus 로고    scopus 로고
    • Shannon sampling II: Connections to learning theory
    • Smale, S., & Zhou, D. X. (2005). Shannon sampling II: Connections to learning theory. Appl. Comput. Harmonic Anal., 19, 285-302.
    • (2005) Appl. Comput. Harmonic Anal. , vol.19 , pp. 285-302
    • Smale, S.1    Zhou, D.X.2
  • 18
    • 34547455409 scopus 로고    scopus 로고
    • Learning theory estimates via integral operators and their applications
    • Smale, S., & Zhou, D. X. (2007). Learning theory estimates via integral operators and their applications. Constr. Approx., 26, 153-172.
    • (2007) Constr. Approx. , vol.26 , pp. 153-172
    • Smale, S.1    Zhou, D.X.2
  • 19
  • 22
    • 33744772341 scopus 로고    scopus 로고
    • Learning rates of least-square regularized regression
    • Wu, Q., Ying, Y., & Zhou, D. X. (2006). Learning rates of least-square regularized regression. Found. Comput. Math., 6, 171-192.
    • (2006) Found. Comput. Math. , vol.6 , pp. 171-192
    • Wu, Q.1    Ying, Y.2    Zhou, D.X.3
  • 23
    • 17444402055 scopus 로고    scopus 로고
    • SVM soft margin classifiers: Linear programming versus quadratic programming
    • Wu, Q., & Zhou, D. X. (2005). SVM soft margin classifiers: Linear programming versus quadratic programming. Neural Comput., 17, 1160-1187.
    • (2005) Neural Comput. , vol.17 , pp. 1160-1187
    • Wu, Q.1    Zhou, D.X.2
  • 24
    • 84954358500 scopus 로고    scopus 로고
    • Learning with sample dependent hypothesis spaces
    • Wu, Q., & Zhou, D. X. (2008). Learning with sample dependent hypothesis spaces. Comput. Math. Appl., 56, 2896-2907.
    • (2008) Comput. Math. Appl. , vol.56 , pp. 2896-2907
    • Wu, Q.1    Zhou, D.X.2
  • 26
    • 0038105204 scopus 로고    scopus 로고
    • Capacity of reproducing kernel spaces in learning theory
    • Zhou, D. X. (2003). Capacity of reproducing kernel spaces in learning theory. IEEE Trans. Inform. Theory, 49, 1743-1752.
    • (2003) IEEE Trans. Inform. Theory , vol.49 , pp. 1743-1752
    • Zhou, D.X.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.