메뉴 건너뛰기




Volumn 14, Issue 3, 2004, Pages 199-222

A tutorial on support vector regression

Author keywords

machine learning; regression estimation; support vector machines

Indexed keywords


EID: 4043137356     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:STCO.0000035301.49549.88     Document Type: Review
Times cited : (9257)

References (140)
  • 1
    • 0000874557 scopus 로고
    • Theoretical foundations of the potential function method in pattern recognition learning
    • Aizerman M.A., Braverman É.M., and Rozonoér L.I. 1964. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25: 821-837.
    • (1964) Automation and Remote Control , vol.25 , pp. 821-837
    • Aizerman, M.A.1    Braverman, É.M.2    Rozonoér, L.I.3
  • 5
    • 0002935122 scopus 로고    scopus 로고
    • Combining support vector and mathematical programming methods for induction
    • Schölkopf B., Burges C.J.C., and Smola A.J., (Eds.), MIT Press, Cambridge, MA
    • Bennett K. 1999. Combining support vector and mathematical programming methods for induction. In: Schölkopf B., Burges C.J.C., and Smola A.J., (Eds.), Advances in Kernel Methods - SV Learning, MIT Press, Cambridge, MA, pp. 307-326.
    • (1999) Advances in Kernel Methods - SV Learning , pp. 307-326
    • Bennett, K.1
  • 6
    • 0026860799 scopus 로고
    • Robust linear programming discrimination of two linearly inseparable sets
    • Bennett K.P. and Mangasarian O.L. 1992. Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software 1: 23-34.
    • (1992) Optimization Methods and Software , vol.1 , pp. 23-34
    • Bennett, K.P.1    Mangasarian, O.L.2
  • 10
    • 84902205493 scopus 로고    scopus 로고
    • Comparison of view-based object recognition algorithms using realistic 3D models
    • von der Malsburg C., von Seelen W., Vorbrüggen J.C., and Sendhoff B. (Eds.), Berlin. Springer Lecture Notes in Computer Science
    • Blanz V., Schölkopf B., Bülthoff H., Burges C., Vapnik V., and Vetter T. 1996. Comparison of view-based object recognition algorithms using realistic 3D models. In: von der Malsburg C., von Seelen W., Vorbrüggen J.C., and Sendhoff B. (Eds.), Artificial Neural Networks ICANN'96, Berlin. Springer Lecture Notes in Computer Science, Vol. 1112, pp. 251-256.
    • (1996) Artificial Neural Networks ICANN'96 , vol.1112 , pp. 251-256
    • Blanz, V.1    Schölkopf, B.2    Bülthoff, H.3    Burges, C.4    Vapnik, V.5    Vetter, T.6
  • 13
    • 0004093835 scopus 로고    scopus 로고
    • Data mining: Overview and optimization opportunities
    • Technical Report 98-01, University of Wisconsin, Computer Sciences Department, Madison, January to appear
    • Bradley P.S., Fayyad U.M., and Mangasarian O.L. 1998. Data mining: Overview and optimization opportunities. Technical Report 98-01, University of Wisconsin, Computer Sciences Department, Madison, January. INFORMS Journal on Computing, to appear.
    • (1998) INFORMS Journal on Computing
    • Bradley, P.S.1    Fayyad, U.M.2    Mangasarian, O.L.3
  • 14
    • 0002709342 scopus 로고    scopus 로고
    • Feature selection via concave minimization and support vector machines
    • Shavlik J. (Ed.), Morgan Kaufmann Publishers, San Francisco, California
    • Bradley P.S. and Mangasarian O.L. 1998. Feature selection via concave minimization and support vector machines. In: Shavlik J. (Ed.), Proceedings of the International Conference on Machine Learning. Morgan Kaufmann Publishers, San Francisco, California, pp. 82-90. ftp://ftp.cs.wisc.edu/math-prog/tech- reports/98-03.ps.Z.
    • (1998) Proceedings of the International Conference on Machine Learning , pp. 82-90
    • Bradley, P.S.1    Mangasarian, O.L.2
  • 15
    • 84966228742 scopus 로고
    • Some stable methods for calculating inertia and solving symmetric linear systems
    • Bunch J.R. and Kaufman L. 1977. Some stable methods for calculating inertia and solving symmetric linear systems. Mathematics of Computation 31: 163-179.
    • (1977) Mathematics of Computation , vol.31 , pp. 163-179
    • Bunch, J.R.1    Kaufman, L.2
  • 18
    • 0002400882 scopus 로고    scopus 로고
    • Simplified support vector decision rules
    • L. Saitta (Ed.), Morgan Kaufmann Publishers, San Mateo, CA
    • Burges C.J.C. 1996. Simplified support vector decision rules. In L. Saitta (Ed.), Proceedings of the International Conference on Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA, pp. 71-77.
    • (1996) Proceedings of the International Conference on Machine Learning , pp. 71-77
    • Burges, C.J.C.1
  • 19
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • Burges C.J.C. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2): 121-167.
    • (1998) Data Mining and Knowledge Discovery , vol.2 , Issue.2 , pp. 121-167
    • Burges, C.J.C.1
  • 20
    • 0002615660 scopus 로고    scopus 로고
    • Geometry and invariance in kernel based methods
    • Schölkopf B., Burges C.J.C., and Smola A.J., (Eds.), MIT Press, Cambridge, MA
    • Burges C.J.C. 1999. Geometry and invariance in kernel based methods. In Schölkopf B., Burges C.J.C., and Smola A.J., (Eds.), Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, MA, pp. 89-116.
    • (1999) Advances in Kernel Methods-Support Vector Learning , pp. 89-116
    • Burges, C.J.C.1
  • 21
    • 84898957872 scopus 로고    scopus 로고
    • Improving the accuracy and speed of support vector learning machines
    • Mozer M.C., Jordan M.I., and Petsche T., (Eds.), MIT Press, Cambridge, MA
    • Burges C.J.C. and Schölkopf B. 1997. Improving the accuracy and speed of support vector learning machines. In Mozer M.C., Jordan M.I., and Petsche T., (Eds.), Advances in Neural Information Processing Systems 9, MIT Press, Cambridge, MA, pp. 375-381.
    • (1997) Advances in Neural Information Processing Systems 9 , pp. 375-381
    • Burges, C.J.C.1    Schölkopf, B.2
  • 22
    • 0346881149 scopus 로고    scopus 로고
    • Experimentally optimal ν in support vector regression for different noise models and parameter settings
    • Chalimourda A., Schölkopf B., and Smola A.J. 2004. Experimentally optimal ν in support vector regression for different noise models and parameter settings. Neural Networks 17(1): 127-141.
    • (2004) Neural Networks , vol.17 , Issue.1 , pp. 127-141
    • Chalimourda, A.1    Schölkopf, B.2    Smola, A.J.3
  • 23
    • 4043161141 scopus 로고    scopus 로고
    • The analysis of decomposition methods for support vector machines
    • SVM Workshop
    • Chang C.-C., Hsu C.-W., and Lin C.-J. 1999. The analysis of decomposition methods for support vector machines. In Proceeding of IJCAI99, SVM Workshop.
    • (1999) Proceeding of IJCAI99
    • Chang, C.-C.1    Hsu, C.-W.2    Lin, C.-J.3
  • 24
    • 0000667930 scopus 로고    scopus 로고
    • Training ν-support vector classifiers: Theory and algorithms
    • Chang C.C. and Lin C.J. 2001. Training ν-support vector classifiers: Theory and algorithms. Neural Computation 13(9): 2119-2147.
    • (2001) Neural Computation , vol.13 , Issue.9 , pp. 2119-2147
    • Chang, C.C.1    Lin, C.J.2
  • 27
    • 34249753618 scopus 로고
    • Support vector networks
    • Cortes C. and Vapnik V. 1995. Support vector networks. Machine Learning 20: 273-297.
    • (1995) Machine Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 28
    • 0000541146 scopus 로고
    • Asymptotic analysis of penalized likelihood and related estimators
    • Cox D. and O'Sullivan F. 1990. Asymptotic analysis of penalized likelihood and related estimators. Annals of Statistics 18: 1676-1695.
    • (1990) Annals of Statistics , vol.18 , pp. 1676-1695
    • Cox, D.1    O'Sullivan, F.2
  • 37
    • 0030541525 scopus 로고    scopus 로고
    • On the formulation and theory of the Newton interior-point method for nonlinear programming
    • El-Bakry A., Tapia R., Tsuchiya R., and Zhang Y. 1996. On the formulation and theory of the Newton interior-point method for nonlinear programming. J. Optimization Theory and Applications 89: 507-541.
    • (1996) J. Optimization Theory and Applications , vol.89 , pp. 507-541
    • El-Bakry, A.1    Tapia, R.2    Tsuchiya, R.3    Zhang, Y.4
  • 39
    • 0000249788 scopus 로고    scopus 로고
    • An equivalence between sparse approximation and support vector machines
    • Girosi F. 1998. An equivalence between sparse approximation and support vector machines. Neural Computation 10(6): 1455-1480.
    • (1998) Neural Computation , vol.10 , Issue.6 , pp. 1455-1480
    • Girosi, F.1
  • 41
    • 0005396750 scopus 로고
    • Automatic capacity tuning of very large VC-dimension classifiers
    • Hanson S.J., Cowan J.D., and Giles C.L. (Eds.), Morgan Kaufmann Publishers
    • Guyon I., Boser B., and Vapnik V. 1993. Automatic capacity tuning of very large VC-dimension classifiers. In: Hanson S.J., Cowan J.D., and Giles C.L. (Eds.), Advances in Neural Information Processing Systems 5. Morgan Kaufmann Publishers, pp. 147-155.
    • (1993) Advances in Neural Information Processing Systems 5. , pp. 147-155
    • Guyon, I.1    Boser, B.2    Vapnik, V.3
  • 47
    • 0000171374 scopus 로고
    • Robust statistics: A review
    • Huber P.J. 1972. Robust statistics: A review. Annals of Statistics 43: 1041.
    • (1972) Annals of Statistics , vol.43 , pp. 1041
    • Huber, P.J.1
  • 48
  • 49
    • 2142669059 scopus 로고
    • IBM optimization subroutine library guide and reference
    • IBM Corporation. 1992. IBM optimization subroutine library guide and reference. IBM Systems Journal, 31, SC23-0519.
    • (1992) IBM Systems Journal , vol.31
  • 51
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale SVM learning practical
    • Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), MIT Press, Cambridge, MA
    • Joachims T. 1999. Making large-scale SVM learning practical. In: Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, MA, pp. 169-184.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 169-184
    • Joachims, T.1
  • 53
    • 0001559380 scopus 로고    scopus 로고
    • Solving the quadratic programming problem arising in support vector classification
    • Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), MIT Press, Cambridge, MA
    • Kaufman L. 1999. Solving the quadratic programming problem arising in support vector classification. In: Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, MA, pp. 147-168
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 147-168
    • Kaufman, L.1
  • 54
    • 0004098720 scopus 로고    scopus 로고
    • Improvements to Platt's SMO algorithm for SVM classifier design
    • Dept. of Mechanical and Production Engineering, Natl. Univ. Singapore, Singapore
    • Keerthi S.S., Shevade S.K., Bhattacharyya C., and Murthy K.R.K. 1999. Improvements to Platt's SMO algorithm for SVM classifier design. Technical Report CD-99-14, Dept. of Mechanical and Production Engineering, Natl. Univ. Singapore, Singapore.
    • (1999) Technical Report CD-99-14
    • Keerthi, S.S.1    Shevade, S.K.2    Bhattacharyya, C.3    Murthy, K.R.K.4
  • 56
    • 0000406385 scopus 로고
    • A correspondence between Bayesian estimation on stochastic processes and smoothing by splines
    • Kimeldorf G.S. and Wahba G. 1970. A correspondence between Bayesian estimation on stochastic processes and smoothing by splines. Annals of Mathematical Statistics 41: 495-502.
    • (1970) Annals of Mathematical Statistics , vol.41 , pp. 495-502
    • Kimeldorf, G.S.1    Wahba, G.2
  • 57
    • 0015000439 scopus 로고
    • Some results on Tchebycheffian spline functions
    • Kimeldorf G.S. and Wahba G. 1971. Some results on Tchebycheffian spline functions. J. Math. Anal. Applic. 33: 82-95.
    • (1971) J. Math. Anal. Applic. , vol.33 , pp. 82-95
    • Kimeldorf, G.S.1    Wahba, G.2
  • 58
    • 0003357515 scopus 로고    scopus 로고
    • Maximal margin perceptron
    • Smola A.J., Bartlett P.L., Schölkopf B., and Schuurmans D. (Eds.), MIT Press, Cambridge, MA
    • Kowalczyk A. 2000. Maximal margin perceptron. In: Smola A.J., Bartlett P.L., Schölkopf B., and Schuurmans D. (Eds.), Advances in Large Margin Classifiers, MIT Press, Cambridge, MA, pp. 75-113.
    • (2000) Advances in Large Margin Classifiers , pp. 75-113
    • Kowalczyk, A.1
  • 61
    • 0003680739 scopus 로고
    • An introduction to Kolmogorov Complexity and its applications
    • Springer, New York
    • Li M. and Vitányi P. 1993. An introduction to Kolmogorov Complexity and its applications. Texts and Monographs in Computer Science. Springer, New York.
    • (1993) Texts and Monographs in Computer Science
    • Li, M.1    Vitányi, P.2
  • 62
    • 0035506741 scopus 로고    scopus 로고
    • On the convergence of the decomposition method for support vector machines
    • Lin C.J. 2001. On the convergence of the decomposition method for support vector machines. IEEE Transactions on Neural Networks 12(6): 1288-1298.
    • (2001) IEEE Transactions on Neural Networks , vol.12 , Issue.6 , pp. 1288-1298
    • Lin, C.J.1
  • 63
    • 0011327883 scopus 로고
    • On implementing Mehrotra's predictor-corrector interior point method for linear programming
    • Dept. of Civil Engineering and Operations Research, Princeton University
    • Lustig I.J., Marsten R.E., and Shanno D.F. 1990. On implementing Mehrotra's predictor-corrector interior point method for linear programming. Princeton Technical Report SOR 90-03., Dept. of Civil Engineering and Operations Research, Princeton University.
    • (1990) Princeton Technical Report SOR 90-03.
    • Lustig, I.J.1    Marsten, R.E.2    Shanno, D.F.3
  • 64
    • 0000022796 scopus 로고
    • On implementing Mehrotra's predictor-corrector interior point method for linear programming
    • Lustig I.J., Marsten R.E., and Shanno D.F. 1992. On implementing Mehrotra's predictor-corrector interior point method for linear programming. SIAM Journal on Optimization 2(3): 435-449.
    • (1992) SIAM Journal on Optimization , vol.2 , Issue.3 , pp. 435-449
    • Lustig, I.J.1    Marsten, R.E.2    Shanno, D.F.3
  • 65
    • 0003748256 scopus 로고
    • PhD thesis, Computation and Neural Systems, California Institute of Technology, Pasadena, CA
    • MacKay D.J.C. 1991. Bayesian Methods for Adaptive Models. PhD thesis, Computation and Neural Systems, California Institute of Technology, Pasadena, CA.
    • (1991) Bayesian Methods for Adaptive Models
    • MacKay, D.J.C.1
  • 66
    • 0000963583 scopus 로고
    • Linear and nonlinear separation of patterns by linear programming
    • Mangasarian O.L. 1965. Linear and nonlinear separation of patterns by linear programming. Operations Research 13: 444-452.
    • (1965) Operations Research , vol.13 , pp. 444-452
    • Mangasarian, O.L.1
  • 69
    • 0002941010 scopus 로고    scopus 로고
    • Support vector machines for dynamic reconstruction of a chaotic system
    • Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), MIT Press, Cambridge, MA
    • Mattera D. and Haykin S. 1999. Support vector machines for dynamic reconstruction of a chaotic system. In: Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, MA, pp. 211-242.
    • (1999) Advances in Kernel Methods-Support Vector Learning , pp. 211-242
    • Mattera, D.1    Haykin, S.2
  • 72
    • 0000561116 scopus 로고
    • On the implementation of a (primal-dual) interior point method
    • Mehrotra S. and Sun J. 1992. On the implementation of a (primal-dual) interior point method. SIAM Journal on Optimization 2(4): 575-601.
    • (1992) SIAM Journal on Optimization , vol.2 , Issue.4 , pp. 575-601
    • Mehrotra, S.1    Sun, J.2
  • 73
    • 0001500115 scopus 로고
    • Functions of positive and negative type and their connection with the theory of integral equations
    • Mercer J. 1909. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society, London A 209: 415-446.
    • (1909) Philosophical Transactions of the Royal Society, London A , vol.209 , pp. 415-446
    • Mercer, J.1
  • 76
    • 84956628443 scopus 로고    scopus 로고
    • Predicting time series with support vector machines
    • Gerstner W., Germond A., Hasler M., and Nicoud J.-D. (Eds.), Berlin. Springer Lecture Notes in Computer Science
    • Müller K.-R., Smola A., Rätsch G., Schölkopf B., Kohlmorgen J., and Vapnik V. 1997. Predicting time series with support vector machines. In: Gerstner W., Germond A., Hasler M., and Nicoud J.-D. (Eds.), Artificial Neural Networks ICANN'97, Berlin. Springer Lecture Notes in Computer Science Vol. 1327 pp. 999-1004.
    • (1997) Artificial Neural Networks ICANN'97 , vol.1327 , pp. 999-1004
    • Müller, K.-R.1    Smola, A.2    Rätsch, G.3    Schölkopf, B.4    Kohlmorgen, J.5    Vapnik, V.6
  • 77
    • 0003446306 scopus 로고
    • Technical Report SOL 83-20R, Stanford University, CA, USA, Revised 1987
    • Murtagh B.A. and Saunders M.A. 1983. MINOS 5.1 user's guide. Technical Report SOL 83-20R, Stanford University, CA, USA, Revised 1987.
    • (1983) MINOS 5.1 User's Guide
    • Murtagh, B.A.1    Saunders, M.A.2
  • 80
    • 79957590676 scopus 로고
    • Certain topics in telegraph transmission theory
    • Nyquist. H. 1928. Certain topics in telegraph transmission theory. Trans. A.I.E.E., pp. 617-644.
    • (1928) Trans. A.I.E.E. , pp. 617-644
    • Nyquist, H.1
  • 82
    • 0001562735 scopus 로고    scopus 로고
    • Reducing the run-time complexity in support vector regression
    • Schölkopf B., Burges C.J.C., and Smola A. J. (Eds.), Cambridge, MA, MIT Press
    • Osuna E. and Girosi F. 1999. Reducing the run-time complexity in support vector regression. In: Schölkopf B., Burges C.J.C., and Smola A. J. (Eds.), Advances in Kernel Methods - Support Vector Learning, pp. 271-284, Cambridge, MA, MIT Press.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 271-284
    • Osuna, E.1    Girosi, F.2
  • 84
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), Cambridge, MA, MIT Press
    • Platt J. 1999. Fast training of support vector machines using sequential minimal optimization. In: Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.) Advances in Kernel Methods - Support Vector Learning, pp. 185-208, Cambridge, MA, MIT Press.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 185-208
    • Platt, J.1
  • 85
    • 0016765357 scopus 로고
    • On optimal nonlinear associative recall
    • Poggio T. 1975. On optimal nonlinear associative recall. Biological Cybernetics, 19: 201-209.
    • (1975) Biological Cybernetics , vol.19 , pp. 201-209
    • Poggio, T.1
  • 87
    • 0018015137 scopus 로고
    • Modeling by shortest data description
    • Rissanen J. 1978. Modeling by shortest data description. Automatica, 14: 465-471.
    • (1978) Automatica , vol.14 , pp. 465-471
    • Rissanen, J.1
  • 89
    • 0004296379 scopus 로고    scopus 로고
    • Support vector machine-reference manual
    • Department of Computer Science, Royal Holloway, University of London, Egham, UK. SVM
    • Saunders C., Stitson M.O., Weston J., Bottou L., Schölkopf B., and Smola A. 1998. Support vector machine-reference manual. Technical Report CSD-TR-98-03, Department of Computer Science, Royal Holloway, University of London, Egham, UK. SVM available at http://svm.dcs.rhbnc.ac.uk/.
    • (1998) Technical Report CSD-TR-98-03
    • Saunders, C.1    Stitson, M.O.2    Weston, J.3    Bottou, L.4    Schölkopf, B.5    Smola, A.6
  • 90
    • 0001878701 scopus 로고
    • Positive definite functions on spheres
    • Schoenberg I. 1942. Positive definite functions on spheres. Duke Math. J., 9: 96-108.
    • (1942) Duke Math. J. , vol.9 , pp. 96-108
    • Schoenberg, I.1
  • 91
    • 84856983285 scopus 로고    scopus 로고
    • R. Oldenbourg Verlag, München. Doktorarbeit, TU Berlin. Download
    • Schölkopf B. 1997. Support Vector Learning. R. Oldenbourg Verlag, München. Doktorarbeit, TU Berlin. Download: http://www.kernel-machines.org.
    • (1997) Support Vector Learning
    • Schölkopf, B.1
  • 93
    • 84902142380 scopus 로고    scopus 로고
    • Incorporating invariances in support vector learning machines
    • von der Malsburg C., von Seelen W., Vorbrüggen J. C., and Sendhoff B. (Eds.), Berlin, Springer Lecture Notes in Computer Science
    • Schölkopf B., Burges C., and Vapnik V. 1996. Incorporating invariances in support vector learning machines. In: von der Malsburg C., von Seelen W., Vorbrüggen J. C., and Sendhoff B. (Eds.), Artificial Neural Networks ICANN'96, pp. 47-52, Berlin, Springer Lecture Notes in Computer Science, Vol. 1112.
    • (1996) Artificial Neural Networks ICANN'96 , vol.1112 , pp. 47-52
    • Schölkopf, B.1    Burges, C.2    Vapnik, V.3
  • 95
    • 7544240447 scopus 로고    scopus 로고
    • A generalized representer theorem
    • NeuroCOLT, 2000. To appear in Proceedings of the Annual Conference on Learning Theory, Springer (2001)
    • Schölkopf B., Herbrich R., Smola A.J., and Williamson R.C. 2001. A generalized representer theorem. Technical Report 2000-81. NeuroCOLT, 2000. To appear in Proceedings of the Annual Conference on Learning Theory, Springer (2001).
    • (2001) Technical Report 2000-81
    • Schölkopf, B.1    Herbrich, R.2    Smola, A.J.3    Williamson, R.C.4
  • 98
    • 51749084180 scopus 로고    scopus 로고
    • Prior knowledge in support vector kernels
    • Jordan M.I., Kearns M.J., and Solla S.A. (Eds.), MIT Press. Cambridge, MA
    • Schölkopf B., Simard P., Smola A., and Vapnik V. 1998a. Prior knowledge in support vector kernels. In: Jordan M.I., Kearns M.J., and Solla S.A. (Eds.) Advances in Neural Information Processing Systems 10, MIT Press. Cambridge, MA, pp. 640-646.
    • (1998) Advances in Neural Information Processing Systems 10 , pp. 640-646
    • Schölkopf, B.1    Simard, P.2    Smola, A.3    Vapnik, V.4
  • 99
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear component analysis as a kernel eigenvalue problem
    • Schölkopf B., Smola A., and Müller K.-R. 1998b. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10: 1299-1319.
    • (1998) Neural Computation , vol.10 , pp. 1299-1319
    • Schölkopf, B.1    Smola, A.2    Müller, K.-R.3
  • 103
    • 84856043672 scopus 로고
    • A mathematical theory of communication
    • Shannon C.E. 1948. A mathematical theory of communication. Bell System Technical Journal, 27: 379-423, 623-656.
    • (1948) Bell System Technical Journal , vol.27 , pp. 379-423
    • Shannon, C.E.1
  • 106
    • 0032098361 scopus 로고    scopus 로고
    • The connection between regularization operators and support vector kernels
    • Smola A., Schölkopf B., and Müller K.-R. 1998b. The connection between regularization operators and support vector kernels. Neural Networks, 11: 637-649.
    • (1998) Neural Networks , vol.11 , pp. 637-649
    • Smola, A.1    Schölkopf, B.2    Müller, K.-R.3
  • 107
    • 0006503735 scopus 로고    scopus 로고
    • General cost functions for support vector regression
    • Downs T., Frean M., and Gallagher M. (Eds.), Brisbane, Australia. University of Queensland
    • Smola A., Schölkopf B., and Müller K.-R. 1998c. General cost functions for support vector regression. In: Downs T., Frean M., and Gallagher M. (Eds.) Proc. of the Ninth Australian Conf. on Neural Networks, pp. 79-83, Brisbane, Australia. University of Queensland.
    • (1998) Proc. of the Ninth Australian Conf. on Neural Networks , pp. 79-83
    • Smola, A.1    Schölkopf, B.2    Müller, K.-R.3
  • 110
    • 0004094721 scopus 로고    scopus 로고
    • PhD thesis, Technische Universität Berlin. GMD Research Series No. 25
    • Smola A.J. 1998. Learning with Kernels. PhD thesis, Technische Universität Berlin. GMD Research Series No. 25.
    • (1998) Learning with Kernels
    • Smola, A.J.1
  • 111
    • 0003093256 scopus 로고    scopus 로고
    • Entropy numbers for convex combinations and MLPs
    • Smola A.J., Bartlett PL., Schölkopf B., and Schuurmans D. (Eds.), MIT Press, Cambridge, MA
    • Smola A.J., Elisseeff A., Schölkopf B., and Williamson R.C. 2000. Entropy numbers for convex combinations and MLPs. In Smola A.J., Bartlett PL., Schölkopf B., and Schuurmans D. (Eds.) Advances in Large Margin Classifiers, MIT Press, Cambridge, MA, pp. 369-387.
    • (2000) Advances in Large Margin Classifiers , pp. 369-387
    • Smola, A.J.1    Elisseeff, A.2    Schölkopf, B.3    Williamson, R.C.4
  • 112
    • 84898955546 scopus 로고    scopus 로고
    • Regularization with dot-product kernels
    • Leen T.K., Dietterich T.G., and Tresp V. (Eds.), MIT Press
    • Smola A.J., Óvári Z.L., and Williamson R.C. 2001. Regularization with dot-product kernels. In: Leen T.K., Dietterich T.G., and Tresp V. (Eds.) Advances in Neural Information Processing Systems 13, MIT Press, pp. 308-314.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 308-314
    • Smola, A.J.1    Óvári, Z.L.2    Williamson, R.C.3
  • 113
    • 24044515976 scopus 로고    scopus 로고
    • On a kernel-based method for pattern recognition, regression, approximation and operator inversion
    • Smola A.J. and Schölkopf B. 1998a. On a kernel-based method for pattern recognition, regression, approximation and operator inversion. Algorithmica, 22: 211-231.
    • (1998) Algorithmica , vol.22 , pp. 211-231
    • Smola, A.J.1    Schölkopf, B.2
  • 114
    • 0003401675 scopus 로고    scopus 로고
    • A tutorial on support vector regression
    • Royal Holloway College, University of London, UK
    • Smola A.J. and Schölkopf B. 1998b. A tutorial on support vector regression. NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK.
    • (1998) NeuroCOLT Technical Report NC-TR-98-030
    • Smola, A.J.1    Schölkopf, B.2
  • 115
    • 0002493574 scopus 로고    scopus 로고
    • Sparse greedy matrix approximation for machine learning
    • Langley P. (Ed.), Morgan Kaufmann Publishers, San Francisco
    • Smola A.J. and Schölkopf B. 2000. Sparse greedy matrix approximation for machine learning. In: Langley P. (Ed.), Proceedings of the International Conference on Machine Learning, Morgan Kaufmann Publishers, San Francisco, pp. 911-918.
    • (2000) Proceedings of the International Conference on Machine Learning , pp. 911-918
    • Smola, A.J.1    Schölkopf, B.2
  • 116
    • 0002081773 scopus 로고    scopus 로고
    • Support vector regression with ANOVA decomposition kernels
    • Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), MIT Press Cambridge, MA
    • Stitson M., Gammerman A., Vapnik V., Vovk V., Watkins C., and Weston J. 1999. Support vector regression with ANOVA decomposition kernels. In: Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), Advances in Kernel Methods - Support Vector Learning, MIT Press Cambridge, MA, pp. 285-292.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 285-292
    • Stitson, M.1    Gammerman, A.2    Vapnik, V.3    Vovk, V.4    Watkins, C.5    Weston, J.6
  • 117
    • 0001227575 scopus 로고
    • Additive regression and other nonparametric models
    • Stone C.J. 1985. Additive regression and other nonparametric models. Annals of Statistics, 13: 689-705.
    • (1985) Annals of Statistics , vol.13 , pp. 689-705
    • Stone, C.J.1
  • 118
    • 0000629975 scopus 로고
    • Cross-validatory choice and assessment of statistical predictors
    • with discussion
    • Stone M. 1974. Cross-validatory choice and assessment of statistical predictors (with discussion). Journal of the Royal Statistical Society, B36: 111-147.
    • (1974) Journal of the Royal Statistical Society , vol.B36 , pp. 111-147
    • Stone, M.1
  • 119
    • 4043141291 scopus 로고
    • Improved generalization via tolerant training
    • University of Wisconsin, Madison
    • Street W.N. and Mangasarian O.L. 1995. Improved generalization via tolerant training. Technical Report MP-TR-95-11, University of Wisconsin, Madison.
    • (1995) Technical Report MP-TR-95-11
    • Street, W.N.1    Mangasarian, O.L.2
  • 121
    • 84899032239 scopus 로고    scopus 로고
    • The relevance vector machine
    • Solla S.A., Leen T.K., and Müller K.-R. (Eds.), MIT Press, Cambridge, MA
    • Tipping M.E. 2000. The relevance vector machine. In: Solla S.A., Leen T.K., and Müller K.-R. (Eds.), Advances in Neural Information Processing Systems 12, MIT Press, Cambridge, MA, pp. 652-658.
    • (2000) Advances in Neural Information Processing Systems , vol.12 , pp. 652-658
    • Tipping, M.E.1
  • 123
    • 0003664630 scopus 로고    scopus 로고
    • LOQO user's manual - Version 3.10
    • Princeton University, Statistics and Operations Research, Code
    • Vanderbei R.J. 1997. LOQO user's manual - version 3.10. Technical Report SOR-97-08, Princeton University, Statistics and Operations Research, Code available at http://www.princeton.edu/ ~rvdb/.
    • (1997) Technical Report SOR-97-08
    • Vanderbei, R.J.1
  • 126
    • 0002817067 scopus 로고    scopus 로고
    • Three remarks on the support vector method of function estimation
    • Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), MIT Press, Cambridge, MA
    • Vapnik. V. 1999. Three remarks on the support vector method of function estimation. In: Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, MA, pp. 25-42.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 25-42
    • Vapnik, V.1
  • 130
    • 84887252594 scopus 로고    scopus 로고
    • Support vector method for function approximation, regression estimation, and signal processing
    • Mozer M.C., Jordan M.I., and Petsche T. (Eds.), MA, MIT Press, Cambridge
    • Vapnik V., Golowich S., and Smola A. 1997. Support vector method for function approximation, regression estimation, and signal processing. In: Mozer M.C., Jordan M.I., and Petsche T. (Eds.) Advances in Neural Information Processing Systems 9, MA, MIT Press, Cambridge. pp. 281-287.
    • (1997) Advances in Neural Information Processing Systems , vol.9 , pp. 281-287
    • Vapnik, V.1    Golowich, S.2    Smola, A.3
  • 131
    • 0010864753 scopus 로고
    • Pattern recognition using generalized portrait method
    • Vapnik V. and Lerner A. 1963. Pattern recognition using generalized portrait method. Automation and Remote Control, 24: 774-780.
    • (1963) Automation and Remote Control , vol.24 , pp. 774-780
    • Vapnik, V.1    Lerner, A.2
  • 133
    • 0001024505 scopus 로고
    • On the uniform convergence of relative frequencies of events to their probabilities
    • Vapnik V.N. and Chervonenkis A.Y. 1971. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, 16(2): 264-281.
    • (1971) Theory of Probability and Its Applications , vol.16 , Issue.2 , pp. 264-281
    • Vapnik, V.N.1    Chervonenkis, A.Y.2
  • 134
    • 0005173689 scopus 로고
    • Spline bases, regularization, and generalized cross-validation for solving approximation problems with large quantities of noisy data
    • Ward J. and Cheney E. (Eds.), Academic Press, Austin, TX
    • Wahba G. 1980. Spline bases, regularization, and generalized cross-validation for solving approximation problems with large quantities of noisy data. In: Ward J. and Cheney E. (Eds.), Proceedings of the International Conference on Approximation theory in honour of George Lorenz, Academic Press, Austin, TX, pp. 8-10.
    • (1980) Proceedings of the International Conference on Approximation Theory in Honour of George Lorenz , pp. 8-10
    • Wahba, G.1
  • 136
    • 0001873883 scopus 로고    scopus 로고
    • Support vector machines, reproducing kernel Hubert spaces and the randomized GACV
    • Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), MIT Press, Cambridge, MA
    • Wahba G. 1999. Support vector machines, reproducing kernel Hubert spaces and the randomized GACV. In: Schölkopf B., Burges C.J.C., and Smola A.J. (Eds.), Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, MA. pp. 69-88.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 69-88
    • Wahba, G.1
  • 138
    • 0003017575 scopus 로고    scopus 로고
    • Prediction with Gaussian processes: From linear regression to linear prediction and beyond
    • Jordan M.I. (Ed.), Kluwer Academic
    • Williams C.K.I. 1998. Prediction with Gaussian processes: From linear regression to linear prediction and beyond. In: Jordan M.I. (Ed.), Learning and Inference in Graphical Models, Kluwer Academic, pp. 599-621.
    • (1998) Learning and Inference in Graphical Models , pp. 599-621
    • Williams, C.K.I.1
  • 139
    • 0035441827 scopus 로고    scopus 로고
    • Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators
    • Technical Report 19, NeuroCOLT, Published (2001)
    • Williamson R.C., Smola A.J., and Schölkopf B. 1998. Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. Technical Report 19, NeuroCOLT, http://www.neurocolt.com. Published in IEEE Transactions on Information Theory, 47(6): 2516-2532 (2001).
    • (1998) IEEE Transactions on Information Theory , vol.47 , Issue.6 , pp. 2516-2532
    • Williamson, R.C.1    Smola, A.J.2    Schölkopf, B.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.