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Volumn 87, Issue 3, 2012, Pages 381-407

Efficient cross-validation for kernelized least-squares regression with sparse basis expansions

Author keywords

Cross validation; Hold out; Kernel methods; Least squares support vector machine; Regularized least squares; Sparse basis expansions

Indexed keywords

CROSS VALIDATION; HOLD-OUT; KERNEL METHODS; LEAST SQUARE; LEAST SQUARES SUPPORT VECTOR MACHINES; SPARSE BASIS;

EID: 84862027224     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-012-5287-6     Document Type: Article
Times cited : (15)

References (41)
  • 1
    • 79751536508 scopus 로고    scopus 로고
    • On learning and cross-validation with decomposed Nyström approximation of kernel matrix
    • 10.1007/s11063-010-9159-4
    • A. Airola T. Pahikkala T. Salakoski 2011 On learning and cross-validation with decomposed Nyström approximation of kernel matrix Neural Processing Letters 33 1 17 30 10.1007/s11063-010-9159-4
    • (2011) Neural Processing Letters , vol.33 , Issue.1 , pp. 17-30
    • Airola, A.1    Pahikkala, T.2    Salakoski, T.3
  • 2
    • 34147111649 scopus 로고    scopus 로고
    • Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression
    • 1115.68125 10.1016/j.patcog.2006.12.015
    • S. An W. Liu S. Venkatesh 2007 Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression Pattern Recognition 40 8 2154 2162 1115.68125 10.1016/j.patcog.2006.12.015
    • (2007) Pattern Recognition , vol.40 , Issue.8 , pp. 2154-2162
    • An, S.1    Liu, W.2    Venkatesh, S.3
  • 3
    • 80052866161 scopus 로고    scopus 로고
    • Incremental and decremental support vector machine learning
    • T. K. Leen T. G. Dietterich V. Tresp (eds). MIT Press Cambridge
    • Cauwenberghs, G., & Poggio, T. (2001). Incremental and decremental support vector machine learning. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems (Vol. 13, pp. 409-415). Cambridge: MIT Press.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 409-415
    • Cauwenberghs, G.1    Poggio, T.2
  • 4
    • 8444241860 scopus 로고    scopus 로고
    • Fast exact leave-one-out cross-validation of sparse least-squares support vector machines
    • 1073.68072 10.1016/j.neunet.2004.07.002
    • G. C. Cawley N. L. C. Talbot 2004 Fast exact leave-one-out cross-validation of sparse least-squares support vector machines Neural Networks 17 10 1467 1475 1073.68072 10.1016/j.neunet.2004.07.002
    • (2004) Neural Networks , vol.17 , Issue.10 , pp. 1467-1475
    • Cawley, G.C.1    Talbot, N.L.C.2
  • 6
    • 0000259511 scopus 로고    scopus 로고
    • Approximate statistical tests for comparing supervised classification learning algorithms
    • 10.1162/089976698300017197
    • T. G. Dietterich 1998 Approximate statistical tests for comparing supervised classification learning algorithms Neural Computation 10 7 1895 1923 10.1162/089976698300017197
    • (1998) Neural Computation , vol.10 , Issue.7 , pp. 1895-1923
    • Dietterich, T.G.1
  • 8
    • 84952149204 scopus 로고
    • A statistical view of some chemometrics regression tools
    • 0775.62288 10.2307/1269656
    • I. E. Frank J. H. Friedman 1993 A statistical view of some chemometrics regression tools Technometrics 35 2 109 135 0775.62288 10.2307/1269656
    • (1993) Technometrics , vol.35 , Issue.2 , pp. 109-135
    • Frank, I.E.1    Friedman, J.H.2
  • 9
    • 0004236492 scopus 로고
    • 2 Johns Hopkins University Press Baltimore 0733.65016
    • Golub, G. H., & Van Loan, C. (1989). Matrix computations (2nd ed.). Baltimore: Johns Hopkins University Press.
    • (1989) Matrix Computations
    • Golub, G.H.1    Van Loan, C.2
  • 11
    • 0004151494 scopus 로고
    • Cambridge University Press Cambridge 0576.15001
    • Horn, R., & Johnson, C. (1985). Matrix analysis. Cambridge: Cambridge University Press.
    • (1985) Matrix Analysis
    • Horn, R.1    Johnson, C.2
  • 12
    • 70450206749 scopus 로고    scopus 로고
    • Efficient leave-m-out cross-validation of support vector regression by generalizing decremental algorithm
    • 1185.68535 10.1007/s00354-008-0067-3
    • M. Karasuyama I. Takeuchi R. Nakano 2009 Efficient leave-m-out cross-validation of support vector regression by generalizing decremental algorithm New Generation Computing 27 307 318 1185.68535 10.1007/s00354-008- 0067-3
    • (2009) New Generation Computing , vol.27 , pp. 307-318
    • Karasuyama, M.1    Takeuchi, I.2    Nakano, R.3
  • 13
    • 85164392958 scopus 로고
    • A study of cross-validation and bootstrap for accuracy estimation and model selection
    • C. Mellish (eds). Morgan Kaufmann San Mateo
    • Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In C. Mellish (Ed.), Proceedings of the fourteenth international joint conference on artificial intelligence (Vol. 2, pp. 1137-1143). San Mateo: Morgan Kaufmann.
    • (1995) Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence , vol.2 , pp. 1137-1143
    • Kohavi, R.1
  • 15
    • 0042847140 scopus 로고    scopus 로고
    • Inference for the generalization error
    • 1039.68104 10.1023/A:1024068626366
    • C. Nadeau Y. Bengio 2003 Inference for the generalization error Machine Learning 52 3 239 281 1039.68104 10.1023/A:1024068626366
    • (2003) Machine Learning , vol.52 , Issue.3 , pp. 239-281
    • Nadeau, C.1    Bengio, Y.2
  • 16
  • 18
    • 59349098809 scopus 로고    scopus 로고
    • Matrix representations, linear transformations, and kernels for disambiguation in natural language
    • 10.1007/s10994-008-5082-6
    • T. Pahikkala S. Pyysalo J. Boberg J. Järvinen T. Salakoski 2009 Matrix representations, linear transformations, and kernels for disambiguation in natural language Machine Learning 74 2 133 158 10.1007/s10994-008-5082-6
    • (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
  • 20
    • 60949112451 scopus 로고    scopus 로고
    • An efficient algorithm for learning to rank from preference graphs
    • 10.1007/s10994-008-5097-z
    • T. Pahikkala E. Tsivtsivadze A. Airola J. Järvinen J. Boberg 2009 An efficient algorithm for learning to rank from preference graphs Machine Learning 75 1 129 165 10.1007/s10994-008-5097-z
    • (2009) Machine Learning , vol.75 , Issue.1 , pp. 129-165
    • Pahikkala, T.1    Tsivtsivadze, E.2    Airola, A.3    Järvinen, J.4    Boberg, J.5
  • 21
    • 27844500576 scopus 로고    scopus 로고
    • The differogram: Non-parametric noise variance estimation and its use for model selection
    • 10.1016/j.neucom.2005.02.015
    • K. Pelckmans J. De Brabanter J. Suykens B. De Moor 2005 The differogram: non-parametric noise variance estimation and its use for model selection Neurocomputing 69 1-3 100 122 10.1016/j.neucom.2005.02.015
    • (2005) Neurocomputing , vol.69 , Issue.13 , pp. 100-122
    • Pelckmans, K.1    De Brabanter, J.2    Suykens, J.3    De Moor, B.4
  • 22
    • 33644990982 scopus 로고    scopus 로고
    • Additive regularization trade-off: Fusion of training and validation levels in kernel methods
    • 10.1007/s10994-005-5315-x
    • K. Pelckmans J. Suykens B. De Moor 2006 Additive regularization trade-off: fusion of training and validation levels in kernel methods Machine Learning 62 217 252 10.1007/s10994-005-5315-x
    • (2006) Machine Learning , vol.62 , pp. 217-252
    • Pelckmans, K.1    Suykens, J.2    De Moor, B.3
  • 23
    • 0025490985 scopus 로고
    • Networks for approximation and learning
    • 10.1109/5.58326
    • T. Poggio F. Girosi 1990 Networks for approximation and learning Proceedings of the IEEE 78 9 1481 1497 10.1109/5.58326
    • (1990) Proceedings of the IEEE , vol.78 , Issue.9 , pp. 1481-1497
    • Poggio, T.1    Girosi, F.2
  • 24
    • 0242705996 scopus 로고    scopus 로고
    • The mathematics of learning: Dealing with data
    • 1968413 1083.68100
    • T. Poggio S. Smale 2003 The mathematics of learning: Dealing with data Notices of the American Mathematical Society 50 5 537 544 1968413 1083.68100
    • (2003) Notices of the American Mathematical Society , vol.50 , Issue.5 , pp. 537-544
    • Poggio, T.1    Smale, S.2
  • 27
    • 56749117943 scopus 로고    scopus 로고
    • In defense of one-vs-all classification
    • 2247975 1222.68287
    • R. Rifkin A. Klautau 2004 In defense of one-vs-all classification Journal of Machine Learning Research 5 101 141 2247975 1222.68287
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 101-141
    • Rifkin, R.1    Klautau, A.2
  • 31
    • 0034336808 scopus 로고    scopus 로고
    • Ten more years of error rate research
    • 1107.62339 10.1111/j.1751-5823.2000.tb00332.x
    • R. A. Schiavo D. J. Hand 2000 Ten more years of error rate research International Statistical Review 68 3 295 310 1107.62339 10.1111/j.1751-5823. 2000.tb00332.x
    • (2000) International Statistical Review , vol.68 , Issue.3 , pp. 295-310
    • Schiavo, R.A.1    Hand, D.J.2
  • 34
    • 84899000575 scopus 로고    scopus 로고
    • Sparse greedy gaussian process regression
    • T. K. Leen T. G. Dietterich V. Tresp (eds). MIT Press Cambridge
    • Smola, A., & Bartlett, P. (2001). Sparse greedy gaussian process regression. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems (Vol. 3, pp. 619-625). Cambridge: MIT Press.
    • (2001) Advances in Neural Information Processing Systems , vol.3 , pp. 619-625
    • Smola, A.1    Bartlett, P.2
  • 35
    • 0032638628 scopus 로고    scopus 로고
    • Least squares support vector machine classifiers
    • 1721843 10.1023/A:1018628609742
    • J. Suykens J. Vandewalle 1999 Least squares support vector machine classifiers Neural Processing Letters 9 3 293 300 1721843 10.1023/A: 1018628609742
    • (1999) Neural Processing Letters , vol.9 , Issue.3 , pp. 293-300
    • Suykens, J.1    Vandewalle, J.2
  • 36
    • 0033334209 scopus 로고    scopus 로고
    • Multiclass least squares support vector machines
    • Inst. Elect. Electronics Eng. New York
    • Suykens, J., & Vandewalle, J. (1999b). Multiclass least squares support vector machines. In International joint conference on neural networks (IJCNN'99) (Vol. 2, pp. 900-903). New York: Inst. Elect. Electronics Eng.
    • (1999) International Joint Conference on Neural Networks (IJCNN'99) , vol.2 , pp. 900-903
    • Suykens, J.1    Vandewalle, J.2
  • 39
    • 0036643065 scopus 로고    scopus 로고
    • Kernel matching pursuit
    • 0998.68120 10.1023/A:1013955821559
    • P. Vincent Y. Bengio 2002 Kernel matching pursuit Machine Learning 48 165 187 0998.68120 10.1023/A:1013955821559
    • (2002) Machine Learning , vol.48 , pp. 165-187
    • Vincent, P.1    Bengio, Y.2
  • 41
    • 84899010839 scopus 로고    scopus 로고
    • Using the Nyström method to speed up kernel machines
    • T. K. Leen T. G. Dietterich V. Tresp (eds). MIT Press Cambridge
    • Williams, C. K. I., & Seeger, M. (2001). Using the Nyström method to speed up kernel machines. In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Advances in neural information processing systems (Vol. 13, pp. 682-688). Cambridge: MIT Press.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 682-688
    • Williams, C.K.I.1    Seeger, M.2


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