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Volumn 5495 LNCS, Issue , 2009, Pages 350-359

Efficient hold-out for subset of regressors

Author keywords

[No Author keywords available]

Indexed keywords

BASIS VECTOR; COMPUTATIONALLY EFFICIENT; CROSS VALIDATION; CROSS-VALIDATIONS; LEAST SQUARE; LEAVE-ONE-OUT; MACHINE LEARNING ALGORITHMS; MODEL SELECTION; OPTIMAL PARAMETER; PERFORMANCE ASSESSMENT; REGULARIZATION PARAMETERS; TRAINING EXAMPLE; TRAINING SETS;

EID: 78650747993     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04921-7_36     Document Type: Conference Paper
Times cited : (5)

References (16)
  • 3
    • 0032638628 scopus 로고    scopus 로고
    • Least squares support vector machine classifiers
    • Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Processing Letters 9(3), 293-300 (1999)
    • (1999) Neural Processing Letters , vol.9 , Issue.3 , pp. 293-300
    • Suykens, J.A.K.1    Vandewalle, J.2
  • 5
    • 59349098809 scopus 로고    scopus 로고
    • Matrix representations, linear transformations, and kernels for disambiguation in natural language
    • Pahikkala, T., Pyysalo, S., Boberg, J., Järvinen, J., Salakoski, T.: Matrix representations, linear transformations, and kernels for disambiguation in natural language. Machine Learning 74(2), 133-158 (2009)
    • (2009) Machine Learning , vol.74 , Issue.2 , pp. 133-158
    • Pahikkala, T.1    Pyysalo, S.2    Boberg, J.3    Järvinen, J.4    Salakoski, T.5
  • 9
    • 8444241860 scopus 로고    scopus 로고
    • Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
    • Cawley, G.C., Talbot, N.L.C.: Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Networks 17(10), 1467-1475 (2004)
    • (2004) Neural Networks , vol.17 , Issue.10 , pp. 1467-1475
    • Cawley, G.C.1    Talbot, N.L.C.2
  • 11
    • 34147111649 scopus 로고    scopus 로고
    • Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression
    • An, S., Liu, W., Venkatesh, S.: Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression. Pattern Recognition 40(8), 2154-2162 (2007)
    • (2007) Pattern Recognition , vol.40 , Issue.8 , pp. 2154-2162
    • An, S.1    Liu, W.2    Venkatesh, S.3
  • 12
    • 44649153951 scopus 로고    scopus 로고
    • Technical Report MIT-CSAIL-TR-2007-025, Massachusetts Institute of Technology
    • Rifkin, R., Lippert, R.: Notes on regularized least squares. Technical Report MIT-CSAIL-TR-2007-025, Massachusetts Institute of Technology (2007)
    • (2007) Notes on Regularized Least Squares
    • Rifkin, R.1    Lippert, R.2
  • 15
    • 84865131152 scopus 로고    scopus 로고
    • A generalized representer theorem
    • Helmbold, D., Williamson, R. (eds.) COLT 2001 and EuroCOLT 2001. Springer, Heidelberg
    • Schölkopf, B., Herbrich, R., Smola, A.J.: A generalized representer theorem. In: Helmbold, D., Williamson, R. (eds.) COLT 2001 and EuroCOLT 2001. LNCS, vol. 2111, pp. 416-426. Springer, Heidelberg (2001)
    • (2001) LNCS , vol.2111 , pp. 416-426
    • Schölkopf, B.1    Herbrich, R.2    Smola, A.J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.