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Volumn 36, Issue 3, 2008, Pages 1171-1220

Kernel methods in machine learning

Author keywords

Graphical models; Machine learning; Reproducing kernels; Support vector machines

Indexed keywords


EID: 51049096780     PISSN: 00905364     EISSN: None     Source Type: Journal    
DOI: 10.1214/009053607000000677     Document Type: Review
Times cited : (1986)

References (157)
  • 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. Autom. Remote Control 25 821-837.
    • (1964) Autom. Remote Control , vol.25 , pp. 821-837
    • AIZERMAN, M.A.1    BRAVERMAN, E.M.2    ROZONOÉR, L.I.3
  • 2
    • 51049111286 scopus 로고    scopus 로고
    • ALLWEIN, E. L., SCHAPIRE, R. E. and SINGER, Y. (2000). Reducing multiclass to binary: A unifying approach for margin classifiers. In Proc. 17th International Conf. Machine Learning (P. Langley, ed.) 9-16. Morgan Kaufmann, San Francisco, CA. MR1884092
    • ALLWEIN, E. L., SCHAPIRE, R. E. and SINGER, Y. (2000). Reducing multiclass to binary: A unifying approach for margin classifiers. In Proc. 17th International Conf. Machine Learning (P. Langley, ed.) 9-16. Morgan Kaufmann, San Francisco, CA. MR1884092
  • 3
    • 0027802035 scopus 로고    scopus 로고
    • ALON, N., BEN-DAVID, S., CESA- BIANCHI, N. and HAUSSLER, D. (1993). Scale-sensitive dimensions, uniform convergence, and learnability. In Proc. of the 34rd Annual Symposium on Foundations of Computer Science 292-301. IEEE Computer Society Press, Los Alamitos, CA. MR1328428
    • ALON, N., BEN-DAVID, S., CESA- BIANCHI, N. and HAUSSLER, D. (1993). Scale-sensitive dimensions, uniform convergence, and learnability. In Proc. of the 34rd Annual Symposium on Foundations of Computer Science 292-301. IEEE Computer Society Press, Los Alamitos, CA. MR1328428
  • 4
    • 14344257912 scopus 로고    scopus 로고
    • Gaussian process classification for segmenting and annotating sequences
    • ACM Press. New York
    • ALTUN, Y., HOFMANN, T. and SMOLA, A. J. (2004). Gaussian process classification for segmenting and annotating sequences. In Proc. International Conf. Machine Learning 25-32. ACM Press. New York.
    • (2004) Proc. International Conf. Machine Learning , pp. 25-32
    • ALTUN, Y.1    HOFMANN, T.2    SMOLA, A.J.3
  • 7
    • 5844297152 scopus 로고
    • Theory of reproducing kernels
    • MR0051437
    • ARONSZAJN, N. (1950). Theory of reproducing kernels. Trans. Amer. Math. Soc. 68 337-404, MR0051437
    • (1950) Trans. Amer. Math. Soc , vol.68 , pp. 337-404
    • ARONSZAJN, N.1
  • 8
    • 51049105811 scopus 로고    scopus 로고
    • BACH, F. R. and JORDAN, M. I. (2002). Kernel independent component analysis. J. Mach. Learn. Res. 31-48, MR 1966051
    • BACH, F. R. and JORDAN, M. I. (2002). Kernel independent component analysis. J. Mach. Learn. Res. 31-48, MR 1966051
  • 10
    • 49549139345 scopus 로고
    • The area above the ordinal dominance graph and the area below the receiver operating characteristic graph
    • MR0384214
    • BAMBER, D. (1975). The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. J. Math. Psych. 12 387-415. MR0384214
    • (1975) J. Math. Psych , vol.12 , pp. 387-415
    • BAMBER, D.1
  • 11
    • 84906640298 scopus 로고    scopus 로고
    • BARNDORFF-NIELSEN, O. E. (1978). Information and Exponential Families in Statistical Theory. Wiley, New York. MR0489333
    • BARNDORFF-NIELSEN, O. E. (1978). Information and Exponential Families in Statistical Theory. Wiley, New York. MR0489333
  • 12
    • 0038453192 scopus 로고    scopus 로고
    • Rademacher and gaussian complexities: Risk bounds and structural results
    • MR 1984026
    • BARTLETT, P. L. and MENDELSON, S. (2002). Rademacher and gaussian complexities: Risk bounds and structural results. J. Mach. Learn. Res. 3 463-482. MR 1984026
    • (2002) J. Mach. Learn. Res , vol.3 , pp. 463-482
    • BARTLETT, P.L.1    MENDELSON, S.2
  • 13
    • 14344253490 scopus 로고    scopus 로고
    • Unifying collaborative and content-based filtering
    • ACM Press, New York
    • BASILICO, J. and HOFMANN, T. (2004). Unifying collaborative and content-based filtering. In Proc. Intl. Conf. Machine Learning 65-72. ACM Press, New York.
    • (2004) Proc. Intl. Conf. Machine Learning , pp. 65-72
    • BASILICO, J.1    HOFMANN, T.2
  • 14
    • 0001862769 scopus 로고
    • An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process
    • MR0341782
    • BAUM, L. E. (1972). An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process. Inequalities 3 1-8. MR0341782
    • (1972) Inequalities , vol.3 , pp. 1-8
    • BAUM, L.E.1
  • 15
    • 0038575680 scopus 로고    scopus 로고
    • On the difficulty of approximately maximizing agreements
    • MR1981222
    • BEN-DAVID, S., EIRON, N. and LONG, P. (2003). On the difficulty of approximately maximizing agreements. J. Comput. System Sci. 66 496-514. MR1981222
    • (2003) J. Comput. System Sci , vol.66 , pp. 496-514
    • BEN-DAVID, S.1    EIRON, N.2    LONG, P.3
  • 17
    • 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. Optim. Methods Softw. 1 23-34.
    • (1992) Optim. Methods Softw , vol.1 , pp. 23-34
    • BENNETT, K.P.1    MANGASARIAN, O.L.2
  • 20
    • 51049121226 scopus 로고    scopus 로고
    • BLOOMFIELD, P. and STEIGER, W. (1983). Least Absolute Deviations: Theory. Applications and Algorithms. Birkhäuser, Boston. MR0748483
    • BLOOMFIELD, P. and STEIGER, W. (1983). Least Absolute Deviations: Theory. Applications and Algorithms. Birkhäuser, Boston. MR0748483
  • 21
    • 0001303543 scopus 로고
    • Monotone Funktionen, Stieltjessche Integrale und harmonische Analyse.
    • MR1512856
    • BOCHNER, S. (1933). Monotone Funktionen, Stieltjessche Integrale und harmonische Analyse. Math. Ann. 108 378-410. MR1512856
    • (1933) Math. Ann , vol.108 , pp. 378-410
    • BOCHNER, S.1
  • 23
    • 0026966646 scopus 로고
    • A training algorithm for optimal margin classifiers
    • D. Haussler, ed, ACM Press, Pittsburgh, PA
    • BOSER, B., GUYON, I. and VAPNIK, V. (1992). A training algorithm for optimal margin classifiers. In Proc. Annual Conf. Computational Learning Theory (D. Haussler, ed.) 144-152. ACM Press, Pittsburgh, PA.
    • (1992) Proc. Annual Conf. Computational Learning Theory , pp. 144-152
    • BOSER, B.1    GUYON, I.2    VAPNIK, V.3
  • 24
    • 84924053271 scopus 로고    scopus 로고
    • Theory of classification: A survey of recent advances
    • MR2182250
    • BOUSQUET, O., BOUCHERON, S. and LUGOSI, G. (2005). Theory of classification: A survey of recent advances. ESAIM Probab. Statist. 9 323-375. MR2182250
    • (2005) ESAIM Probab. Statist , vol.9 , pp. 323-375
    • BOUSQUET, O.1    BOUCHERON, S.2    LUGOSI, G.3
  • 25
    • 27144489164 scopus 로고    scopus 로고
    • B URGES, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2 121-167.
    • B URGES, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2 121-167.
  • 26
    • 0032187518 scopus 로고    scopus 로고
    • Blind signal separation: Statistical principles
    • CARDOSO, J.-F. (1998). Blind signal separation: Statistical principles. Proceedings of the IEEE 90 2009-2026.
    • (1998) Proceedings of the IEEE , vol.90 , pp. 2009-2026
    • CARDOSO, J.-F.1
  • 27
    • 31844453941 scopus 로고    scopus 로고
    • A machine learning approach to conjoint analysis
    • L. K. Saul, Y. Weiss and L. Bottou, eds, MIT Press, Cambridge, MA
    • CHAPELLE, O. and HARCHAOUI, Z. (2005). A machine learning approach to conjoint analysis. In Advances in Neural Information Processing Systems 17 (L. K. Saul, Y. Weiss and L. Bottou, eds.) 257-264. MIT Press, Cambridge, MA.
    • (2005) Advances in Neural Information Processing Systems , vol.17 , pp. 257-264
    • CHAPELLE, O.1    HARCHAOUI, Z.2
  • 28
    • 27844461921 scopus 로고    scopus 로고
    • Consistent independent component analysis and prewhitening
    • MR2239886
    • CHEN, A. and BICKEL, P. (2005). Consistent independent component analysis and prewhitening. IEEE Trans. Signal Process. 53 3625-3632. MR2239886
    • (2005) IEEE Trans. Signal Process , vol.53 , pp. 3625-3632
    • CHEN, A.1    BICKEL, P.2
  • 29
    • 0032131292 scopus 로고    scopus 로고
    • Atomic decomposition by basis pursuit
    • MR1639094
    • CHEN, S., DONOHO, D. and SAUNDERS, M. (1999). Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20 33-61. MR1639094
    • (1999) SIAM J. Sci. Comput , vol.20 , pp. 33-61
    • CHEN, S.1    DONOHO, D.2    SAUNDERS, M.3
  • 30
    • 0040044720 scopus 로고    scopus 로고
    • Discriminative reranking for natural language parsing
    • P. Langley, ed, Morgan Kaufmann, San Francisco, CA
    • COLLINS, M. (2000). Discriminative reranking for natural language parsing. In Proc. 17th International Conf. Machine Learning (P. Langley, ed.) 175-182. Morgan Kaufmann, San Francisco, CA.
    • (2000) Proc. 17th International Conf. Machine Learning , pp. 175-182
    • COLLINS, M.1
  • 31
    • 1942419006 scopus 로고    scopus 로고
    • Convolution kernels for natural language
    • T. G. Dietterich, S. Becker and Z. Ghahramani, eds, MIT Press, Cambridge, MA
    • COLLINS, M. and DUFFY, N. (2001). Convolution kernels for natural language. In Advances in Neural Information Processing Systems 14 (T. G. Dietterich, S. Becker and Z. Ghahramani, eds.) 625-632. MIT Press, Cambridge, MA.
    • (2001) Advances in Neural Information Processing Systems , vol.14 , pp. 625-632
    • COLLINS, M.1    DUFFY, N.2
  • 32
    • 84952261476 scopus 로고
    • Projection pursuit indices based on orthonormal function expansions
    • MR1272393
    • COOK, D., BUJA, A. and CABRERA, J. (1993). Projection pursuit indices based on orthonormal function expansions. J. Comput. Graph. Statist. 2 225-250. MR1272393
    • (1993) J. Comput. Graph. Statist , vol.2 , pp. 225-250
    • COOK, D.1    BUJA, A.2    CABRERA, J.3
  • 34
    • 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
  • 35
    • 0010442827 scopus 로고    scopus 로고
    • On the algorithmic implementation of multiclass kernel-based vector machines
    • CRAMMER, K. and SINGER, Y. (2001). On the algorithmic implementation of multiclass kernel-based vector machines. J. Mach. Learn. Res. 2 265-292.
    • (2001) J. Mach. Learn. Res , vol.2 , pp. 265-292
    • CRAMMER, K.1    SINGER, Y.2
  • 36
    • 26944437032 scopus 로고    scopus 로고
    • CRAMMER, K. and SINGER, Y. (2005). Loss bounds for online category ranking. In Proc. Annual Conf. Computational Learning Theory (P. Auer and R. Meir, eds.) 48-62. Springer, Berlin. MR2203253
    • CRAMMER, K. and SINGER, Y. (2005). Loss bounds for online category ranking. In Proc. Annual Conf. Computational Learning Theory (P. Auer and R. Meir, eds.) 48-62. Springer, Berlin. MR2203253
  • 38
    • 84898936871 scopus 로고    scopus 로고
    • CRISTIANINI, N., SHAWE-TAYLOR, J., ELISSEEFF, A. and KANDOLA, J. (2002). On kernel-target alignment. In Advances in Neural Information Processing Systems 14 (T. G. Dietterich, S. Becker and Z. Ghahramani, eds.) 367-373. MIT Press, Cambridge, MA.
    • CRISTIANINI, N., SHAWE-TAYLOR, J., ELISSEEFF, A. and KANDOLA, J. (2002). On kernel-target alignment. In Advances in Neural Information Processing Systems 14 (T. G. Dietterich, S. Becker and Z. Ghahramani, eds.) 367-373. MIT Press, Cambridge, MA.
  • 39
    • 51049119483 scopus 로고    scopus 로고
    • CULOTTA, A., KULP, D. and MCCALLUM, A. (2005). Gene prediction with conditional random fields. Technical Report UM-CS-2005-028, Univ. Massachusetts, Amherst.
    • CULOTTA, A., KULP, D. and MCCALLUM, A. (2005). Gene prediction with conditional random fields. Technical Report UM-CS-2005-028, Univ. Massachusetts, Amherst.
  • 40
    • 0001573124 scopus 로고
    • Generalized iterative scaling for log-linear models
    • MR0345337
    • DARROCH, J. N. and RATCLIFF, D. (1972). Generalized iterative scaling for log-linear models. Ann. Math. Statist. 43 1470-1480. MR0345337
    • (1972) Ann. Math. Statist , vol.43 , pp. 1470-1480
    • DARROCH, J.N.1    RATCLIFF, D.2
  • 41
    • 4744372292 scopus 로고
    • Restricted canonical correlations
    • MR1294769
    • DAS, D. and SEN, P. (1994). Restricted canonical correlations. Linear Algebra Appl. 210 29-47. MR1294769
    • (1994) Linear Algebra Appl , vol.210 , pp. 29-47
    • DAS, D.1    SEN, P.2
  • 42
    • 0040350036 scopus 로고    scopus 로고
    • Nonlinear canonical analysis and independence tests
    • MR1647653
    • DAUXOIS, J. and NKIET, G. M. (1998). Nonlinear canonical analysis and independence tests. Ann. Statist. 26 1254-1278. MR1647653
    • (1998) Ann. Statist , vol.26 , pp. 1254-1278
    • DAUXOIS, J.1    NKIET, G.M.2
  • 43
    • 0003064380 scopus 로고
    • Applications of a general propagation algorithm for probabilistic expert systems
    • DAWID, A. P. (1992). Applications of a general propagation algorithm for probabilistic expert systems. Stat. Comput. 2 25-36.
    • (1992) Stat. Comput , vol.2 , pp. 25-36
    • DAWID, A.P.1
  • 44
    • 0036161034 scopus 로고    scopus 로고
    • Training invariant support vector machines
    • DECOSTE, D. and SCHÖLKOPF, B. (2002). Training invariant support vector machines. Machine Learning 46 161-190.
    • (2002) Machine Learning , vol.46 , pp. 161-190
    • DECOSTE, D.1    SCHÖLKOPF, B.2
  • 45
    • 84898970009 scopus 로고    scopus 로고
    • Log-linear models for label ranking
    • S. Thrun, L. Saul and B. Schölkopf, eds, MIT Press, Cambridge, MA
    • DEKEL, O., MANNING, C. and SINGER, Y. (2004). Log-linear models for label ranking. In Advances in Neural Information Processing Systems 16 (S. Thrun, L. Saul and B. Schölkopf, eds.) 497-504. MIT Press, Cambridge, MA.
    • (2004) Advances in Neural Information Processing Systems , vol.16 , pp. 497-504
    • DEKEL, O.1    MANNING, C.2    SINGER, Y.3
  • 47
    • 0001765899 scopus 로고
    • Generalized quantile processes
    • MR1165606
    • EINMAL, J. H. J. and MASON, D. M. (1992). Generalized quantile processes. Ann. Statist. 20 1062-1078. MR1165606
    • (1992) Ann. Statist , vol.20 , pp. 1062-1078
    • EINMAL, J.H.J.1    MASON, D.M.2
  • 48
    • 2542631648 scopus 로고    scopus 로고
    • A kernel method for multi-labeled classification
    • MIT Press, Cambridge, MA
    • ELISSEEFF, A. and WESTON, J. (2001). A kernel method for multi-labeled classification. In Advances in Neural Information Processing Systems 14 681-687. MIT Press, Cambridge, MA.
    • (2001) Advances in Neural Information Processing Systems , vol.14 , pp. 681-687
    • ELISSEEFF, A.1    WESTON, J.2
  • 49
    • 0001350119 scopus 로고
    • Algebraic connectivity of graphs
    • MR0318007
    • FIEDLER, M. (1973). Algebraic connectivity of graphs. Czechoslovak Math. J. 23 298-305. MR0318007
    • (1973) Czechoslovak Math. J , vol.23 , pp. 298-305
    • FIEDLER, M.1
  • 50
    • 21844525300 scopus 로고
    • Functions that preserve families of positive semidefinite matrices
    • MR1331791
    • FITZGERALD, C. H., MICCHELLI, C. A. and PINKUS, A. (1995). Functions that preserve families of positive semidefinite matrices. Linear Algebra Appl. 221 83-102. MR1331791
    • (1995) Linear Algebra Appl , vol.221 , pp. 83-102
    • FITZGERALD, C.H.1    MICCHELLI, C.A.2    PINKUS, A.3
  • 51
    • 51049090449 scopus 로고    scopus 로고
    • FLETCHER, R. (1989). Practical Methods of Optimization. Wiley, New York. MR0955799
    • FLETCHER, R. (1989). Practical Methods of Optimization. Wiley, New York. MR0955799
  • 52
    • 0000945775 scopus 로고
    • Convergence de la réparation empirique vers la réparation théorique.
    • MR0061325
    • FORTET, R. and MOURIER, E. (1953). Convergence de la réparation empirique vers la réparation théorique. Ann. Scient. École Norm. Sup. 70 266-285. MR0061325
    • (1953) Ann. Scient. École Norm. Sup , vol.70 , pp. 266-285
    • FORTET, R.1    MOURIER, E.2
  • 54
    • 84950754164 scopus 로고
    • Exploratory projection pursuit
    • MR0883353
    • FRIEDMAN, J. H. (1987). Exploratory projection pursuit. J. Amer. Statist. Assoc. 82 249-266. MR0883353
    • (1987) J. Amer. Statist. Assoc , vol.82 , pp. 249-266
    • FRIEDMAN, J.H.1
  • 55
    • 0016102310 scopus 로고
    • A projection pursuit algorithm for exploratory data analysis
    • FRIEDMAN, J. H. and TUKEY, J. W. (1974). A projection pursuit algorithm for exploratory data analysis. IEEE Trans. Comput. C-23 881-890.
    • (1974) IEEE Trans. Comput , vol.C-23 , pp. 881-890
    • FRIEDMAN, J.H.1    TUKEY, J.W.2
  • 56
    • 4444231365 scopus 로고    scopus 로고
    • A survey of kernels for structured data
    • GÄRTNER, T. (2003). A survey of kernels for structured data. SIGKDD Explorations 5 49-58.
    • (2003) SIGKDD Explorations , vol.5 , pp. 49-58
    • GÄRTNER, T.1
  • 58
    • 33646528415 scopus 로고    scopus 로고
    • GRETTON, A., BOUSQUET, O., SMOLA, A. and SCHÖLKOPF, B. (2005). Measuring statistical dependence with Hilbert-Schmidt norms. In Proceedings Algorithmic Learning Theory (S. Jain, H. U. Simon and E. Tomita, eds.) 63-77. Springer, Berlin. MR2255909
    • GRETTON, A., BOUSQUET, O., SMOLA, A. and SCHÖLKOPF, B. (2005). Measuring statistical dependence with Hilbert-Schmidt norms. In Proceedings Algorithmic Learning Theory (S. Jain, H. U. Simon and E. Tomita, eds.) 63-77. Springer, Berlin. MR2255909
  • 59
    • 29144505110 scopus 로고    scopus 로고
    • GRETTON, A., SMOLA, A., BOUSQUET, O., HERBRICH, R., BELITSKI, A., AUGATH, M., MURAYAMA, Y., PAULS, J., SCHÖLKOPF, B. and LOGOTHETIS, N. (2005). Kernel constrained covariance for dependence measurement. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (R. G. Cowell and Z. Ghahramani, eds.) 112-119. Society for Artificial Intelligence and Statistics, New Jersey.
    • GRETTON, A., SMOLA, A., BOUSQUET, O., HERBRICH, R., BELITSKI, A., AUGATH, M., MURAYAMA, Y., PAULS, J., SCHÖLKOPF, B. and LOGOTHETIS, N. (2005). Kernel constrained covariance for dependence measurement. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (R. G. Cowell and Z. Ghahramani, eds.) 112-119. Society for Artificial Intelligence and Statistics, New Jersey.
  • 61
    • 0003678451 scopus 로고
    • Markov fields on finite graphs and lattices
    • Unpublished manuscript
    • HAMMERSLEY, J. M. and CLIFFORD, P. E. (1971). Markov fields on finite graphs and lattices. Unpublished manuscript.
    • (1971)
    • HAMMERSLEY, J.M.1    CLIFFORD, P.E.2
  • 62
    • 0008267184 scopus 로고    scopus 로고
    • Convolutional kernels on discrete structures
    • Technical Report UCSC-CRL-99-10, Computer Science Dept, UC Santa Cruz
    • HAUSSLER, D. (1999). Convolutional kernels on discrete structures. Technical Report UCSC-CRL-99-10, Computer Science Dept., UC Santa Cruz.
    • (1999)
    • HAUSSLER, D.1
  • 63
    • 24644463278 scopus 로고    scopus 로고
    • Maximal margin classification for metric spaces
    • MR2168357
    • HEIN, M., BOUSQUET, O. and SCHÖLKOPF, B. (2005). Maximal margin classification for metric spaces. J. Comput. System Sci. 71 333-359. MR2168357
    • (2005) J. Comput. System Sci , vol.71 , pp. 333-359
    • HEIN, M.1    BOUSQUET, O.2    SCHÖLKOPF, B.3
  • 65
    • 51049091967 scopus 로고    scopus 로고
    • HERBRICH, R., GRAEPEL, T. and OBERMAYER, K. (2000). Large margin rank boundaries for ordinal regression. In Advances in Large Margin Classifiers (A. J. Smola, P. L. Bartlett, B. Schölkopf and D. Schuurmans, eds.) 115-132. MIT Press, Cambridge, MA. MR1820960
    • HERBRICH, R., GRAEPEL, T. and OBERMAYER, K. (2000). Large margin rank boundaries for ordinal regression. In Advances in Large Margin Classifiers (A. J. Smola, P. L. Bartlett, B. Schölkopf and D. Schuurmans, eds.) 115-132. MIT Press, Cambridge, MA. MR1820960
  • 66
    • 0027657329 scopus 로고
    • Semi-infinite programming: Theory, methods, and applications
    • MR1234637
    • HETTICH, R. and KORTANEK, K. O. (1993). Semi-infinite programming: Theory, methods, and applications. SIAM Rev. 35 380-429. MR1234637
    • (1993) SIAM Rev , vol.35 , pp. 380-429
    • HETTICH, R.1    KORTANEK, K.O.2
  • 67
    • 0010705369 scopus 로고
    • Grundzüge einer allgemeinen Theorie der linearen Integralgleichungen
    • HILBERT, D. (1904). Grundzüge einer allgemeinen Theorie der linearen Integralgleichungen. Nachr. Akad. Wiss. Göttingen Math.-Phys. Kl. II 49-91.
    • (1904) Nachr. Akad. Wiss. Göttingen Math.-Phys. Kl. II , pp. 49-91
    • HILBERT, D.1
  • 68
    • 84942484786 scopus 로고
    • Ridge regression: Biased estimation for nonorthogonal problems
    • HOERL, A. E. and KENNARD, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12 55-67.
    • (1970) Technometrics , vol.12 , pp. 55-67
    • HOERL, A.E.1    KENNARD, R.W.2
  • 69
    • 38149004973 scopus 로고    scopus 로고
    • A review of kernel methods in machine learning
    • Technical Report 156, Max-Planck-Institut für biologische Kybernetik
    • HOFMANN, T., SCHÖLKOPF, B. and SMOLA, A. J. (2006). A review of kernel methods in machine learning. Technical Report 156, Max-Planck-Institut für biologische Kybernetik.
    • (2006)
    • HOFMANN, T.1    SCHÖLKOPF, B.2    SMOLA, A.J.3
  • 70
    • 0000107975 scopus 로고
    • Relations between two sets of variates
    • HOTELLING, H. (1936). Relations between two sets of variates. Biometrika 28 321-377.
    • (1936) Biometrika , vol.28 , pp. 321-377
    • HOTELLING, H.1
  • 71
    • 51049124372 scopus 로고    scopus 로고
    • HUBER, P. J. (1981). Robust Statistics. Wiley, New York. MR0606374
    • HUBER, P. J. (1981). Robust Statistics. Wiley, New York. MR0606374
  • 72
    • 0000263797 scopus 로고
    • Projection pursuit
    • MR0790553
    • HUBER, P. J. (1985). Projection pursuit. Ann. Statist. 13 435-475. MR0790553
    • (1985) Ann. Statist , vol.13 , pp. 435-475
    • HUBER, P.J.1
  • 75
    • 9444269199 scopus 로고    scopus 로고
    • Bhattacharyya and expected likelihood kernels
    • Proceedings of the Sixteenth Annual Conference on Computational Learning TheoryB. Scholkopf and M. Warmuth, eds, Springer, Heidelberg
    • JEBARA, T. and KONDOR, I. (2003). Bhattacharyya and expected likelihood kernels. Proceedings of the Sixteenth Annual Conference on Computational Learning Theory(B. Scholkopf and M. Warmuth, eds.) 57-71. Lecture Notes in Comput. Sci.2777. Springer, Heidelberg.
    • (2003) Lecture Notes in Comput. Sci , vol.2777 , pp. 57-71
    • JEBARA, T.1    KONDOR, I.2
  • 76
    • 0001698979 scopus 로고
    • Bayesian updates in causal probabilistic networks by local computation
    • MR1073446
    • JENSEN, F. V., LAURITZEN, S. L. and OLESEN, K. G. (1990). Bayesian updates in causal probabilistic networks by local computation. Comput. Statist. Quaterly 4 269-282. MR1073446
    • (1990) Comput. Statist. Quaterly , vol.4 , pp. 269-282
    • JENSEN, F.V.1    LAURITZEN, S.L.2    OLESEN, K.G.3
  • 78
    • 31844446804 scopus 로고    scopus 로고
    • A support vector method for multivariate performance measures
    • Morgan Kaufmann, San Francisco, CA
    • JOACHIMS, T. (2005). A support vector method for multivariate performance measures. In Proc. Intl. Conf. Machine Learning 377-384. Morgan Kaufmann, San Francisco, CA.
    • (2005) Proc. Intl. Conf. Machine Learning , pp. 377-384
    • JOACHIMS, T.1
  • 80
    • 51049116714 scopus 로고    scopus 로고
    • JORDAN, M. I., BARTLETT, P. L. and. MCAULIFFE, J. D. (2003). Convexity, classification, and risk bounds. Technical Report 638, Univ. California, Berkeley.
    • JORDAN, M. I., BARTLETT, P. L. and. MCAULIFFE, J. D. (2003). Convexity, classification, and risk bounds. Technical Report 638, Univ. California, Berkeley.
  • 82
    • 1942516986 scopus 로고    scopus 로고
    • Marginalized kernels between labeled graphs
    • Morgan Kaufmann, San Francisco, CA
    • KASHIMA, H., TSUDA, K. and INOKUCHI, A. (2003). Marginalized kernels between labeled graphs. In Proc. Intl. Conf. Machine Learning 321-328. Morgan Kaufmann, San Francisco, CA.
    • (2003) Proc. Intl. Conf. Machine Learning , pp. 321-328
    • KASHIMA, H.1    TSUDA, K.2    INOKUCHI, A.3
  • 83
    • 0000020007 scopus 로고
    • Canonical analysis of several sets of variables
    • MR0341750
    • KETTENRING, J. R. (1971). Canonical analysis of several sets of variables. Biometrika 58 433-451. MR0341750
    • (1971) Biometrika , vol.58 , pp. 433-451
    • KETTENRING, J.R.1
  • 85
    • 0015000439 scopus 로고
    • Some results on Tchebycheffian spline functions
    • MR0290013
    • KLMELDORF, G. S. and WAHBA, G. (1971). Some results on Tchebycheffian spline functions. J. Math. Anal. Appl. 33 82-95. MR0290013
    • (1971) J. Math. Anal. Appl , vol.33 , pp. 82-95
    • KLMELDORF, G.S.1    WAHBA, G.2
  • 86
    • 0035397715 scopus 로고    scopus 로고
    • KOLTCHINSKII, V. (2001). Rademacher penalties and structural risk minimization. IEEE Trans. Inform. Theory 47 1902-1914. MR1842526
    • KOLTCHINSKII, V. (2001). Rademacher penalties and structural risk minimization. IEEE Trans. Inform. Theory 47 1902-1914. MR1842526
  • 87
    • 0041775676 scopus 로고    scopus 로고
    • Diffusion kernels on graphs and other discrete structures
    • Morgan Kaufmann, San Francisco, CA
    • KONDOR, I. R. and LAFFERTY, J. D. (2002). Diffusion kernels on graphs and other discrete structures. In Proc. International Conf. Machine Learning 315-322. Morgan Kaufmann, San Francisco, CA.
    • (2002) Proc. International Conf. Machine Learning , pp. 315-322
    • KONDOR, I.R.1    LAFFERTY, J.D.2
  • 89
    • 33646426783 scopus 로고    scopus 로고
    • Kernel conditional random fields: Representation and clique selection
    • Morgan Kaufmann, San Francisco, CA
    • LAFFERTY, J., ZHU, X. and LIU, Y. (2004). Kernel conditional random fields: Representation and clique selection. In Proc. International Conf. Machine Learning 21 64. Morgan Kaufmann, San Francisco, CA.
    • (2004) Proc. International Conf. Machine Learning , vol.21 , pp. 64
    • LAFFERTY, J.1    ZHU, X.2    LIU, Y.3
  • 90
    • 0142192295 scopus 로고    scopus 로고
    • Conditional random fields: Probabilistic modeling for segmenting and labeling sequence data
    • Morgan Kaufmann, San Francisco, CA
    • LAFFERTY, J. D., MCCALLUM, A. and PEREIRA, F. (2001). Conditional random fields: Probabilistic modeling for segmenting and labeling sequence data. In Proc. International Conf. Machine Learning 18 282-289. Morgan Kaufmann, San Francisco, CA.
    • (2001) Proc. International Conf. Machine Learning , vol.18 , pp. 282-289
    • LAFFERTY, J.D.1    MCCALLUM, A.2    PEREIRA, F.3
  • 91
    • 0034207888 scopus 로고    scopus 로고
    • A unifying framework for independent component analysis
    • MR1766376
    • LEE, T.-W., GIROLAMI, M., BELL, A. and SEJNOWSKI, T. (2000). A unifying framework for independent component analysis. Comput. Math. Appl. 39 1-21. MR1766376
    • (2000) Comput. Math. Appl , vol.39 , pp. 1-21
    • LEE, T.-W.1    GIROLAMI, M.2    BELL, A.3    SEJNOWSKI, T.4
  • 92
    • 0036358995 scopus 로고    scopus 로고
    • The spectrum kernel: A string kernel for SVM protein classification
    • World Scientific Publishing, Singapore
    • LESLIE, .C, ESKIN, E. and NOBLE, W. S. (2002). The spectrum kernel: A string kernel for SVM protein classification. In Proceedings of the Pacific Symposium on Biocomputing 564-575. World Scientific Publishing, Singapore.
    • (2002) Proceedings of the Pacific Symposium on Biocomputing , pp. 564-575
    • LESLIE, C.1    ESKIN, E.2    NOBLE, W.S.3
  • 93
    • 51049117793 scopus 로고    scopus 로고
    • LIÈVE, M. (1978). Probability Theory II, 4th ed. Springer, New York. MR0651018
    • LIÈVE, M. (1978). Probability Theory II, 4th ed. Springer, New York. MR0651018
  • 94
    • 84958982834 scopus 로고    scopus 로고
    • Learning grammatical structure using statistical decision-trees
    • Springer, Berlin
    • MAGERMAN, D. M. (1996). Learning grammatical structure using statistical decision-trees. Proceedings ICGI. Lecture Notes in Artificial Intelligence 1147 1-21. Springer, Berlin.
    • (1996) Proceedings ICGI. Lecture Notes in Artificial Intelligence , vol.1147 , pp. 1-21
    • MAGERMAN, D.M.1
  • 95
    • 0000963583 scopus 로고
    • Linear and nonlinear separation of patterns by linear programming
    • MR0192918
    • MANGASARIAN, O. L. (1965). Linear and nonlinear separation of patterns by linear programming. Oper. Res. 13 444-452. MR0192918
    • (1965) Oper. Res , vol.13 , pp. 444-452
    • MANGASARIAN, O.L.1
  • 96
    • 44849098451 scopus 로고    scopus 로고
    • A conditional random field for discriminatively- trained finite-state string edit distance
    • AUAI Press, Arlington, VA
    • MCCALLUM., A., BELLARE, K. and PEREIRA, F. (2005). A conditional random field for discriminatively- trained finite-state string edit distance. In Conference on Uncertainty in AI (UAI)388. AUAI Press, Arlington, VA.
    • (2005) Conference on Uncertainty in AI (UAI) , vol.388
    • MCCALLUM, A.1    BELLARE, K.2    PEREIRA, F.3
  • 97
    • 51049093259 scopus 로고    scopus 로고
    • MCCULLAGH, P. and NELD.ER, J. A. (1983). Generalized Linear Models. Chapman and Hall, London. MR0727836
    • MCCULLAGH, P. and NELD.ER, J. A. (1983). Generalized Linear Models. Chapman and Hall, London. MR0727836
  • 98
    • 35248851077 scopus 로고    scopus 로고
    • A few notes on statistical learning theory
    • Advanced Lectures on Machine Learning S. Mendelson and A. J. Smola, eds, Springer, Heidelberg
    • MENDELSON, S. (2003). A few notes on statistical learning theory. Advanced Lectures on Machine Learning (S. Mendelson and A. J. Smola, eds.). Lecture Notes in Artificial Intelligence 2600 1-40. Springer, Heidelberg.
    • (2003) Lecture Notes in Artificial Intelligence , vol.2600 , pp. 1-40
    • MENDELSON, S.1
  • 99
    • 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. Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. A 209 415-446.
    • (1909) Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. A , vol.209 , pp. 415-446
    • MERCER, J.1
  • 102
    • 51049095316 scopus 로고    scopus 로고
    • MOROZOV, V. A. (1984). Methods for Solving Incorrectly Posed Problems. Springer, New York. MR0766231
    • MOROZOV, V. A. (1984). Methods for Solving Incorrectly Posed Problems. Springer, New York. MR0766231
  • 103
    • 51049084495 scopus 로고    scopus 로고
    • MURRAY, M. K. and RICE, J. W. (1993). Differential Geometry and Statistics. Chapman and Hall, London. MR1293124
    • MURRAY, M. K. and RICE, J. W. (1993). Differential Geometry and Statistics. Chapman and Hall, London. MR1293124
  • 104
    • 51049090660 scopus 로고    scopus 로고
    • OLIVER, N., SCHÖLKOPF, B. and SMOLA, A. J. (2000). Natural regularization in SVMs. In Advances in Large Margin Classifiers (A. J. Smola, P. L. Bartlett, B. Schölkopf and D. Schuurmans, eds.) 51-60. MIT Press, Cambridge, MA. MR1820960
    • OLIVER, N., SCHÖLKOPF, B. and SMOLA, A. J. (2000). Natural regularization in SVMs. In Advances in Large Margin Classifiers (A. J. Smola, P. L. Bartlett, B. Schölkopf and D. Schuurmans, eds.) 51-60. MIT Press, Cambridge, MA. MR1820960
  • 105
    • 0001781878 scopus 로고
    • Automatic smoothing of regression functions in generalized linear models
    • MR0830570
    • O'SULLIVAN, F., YANDELL, B. and RAYNOR, W. (1986). Automatic smoothing of regression functions in generalized linear models. J. Amer. Statist. Assoc. 81 96-103. MR0830570
    • (1986) J. Amer. Statist. Assoc , vol.81 , pp. 96-103
    • O'SULLIVAN, F.1    YANDELL, B.2    RAYNOR, W.3
  • 106
    • 51049105596 scopus 로고    scopus 로고
    • PARZEN, E. (1970). Statistical inference on time series by RKHS methods. In Proceedings 12th Biennial Seminar (R. Pyke, ed.) 1-37. Canadian Mathematical Congress, Montreal. MR0275616
    • PARZEN, E. (1970). Statistical inference on time series by RKHS methods. In Proceedings 12th Biennial Seminar (R. Pyke, ed.) 1-37. Canadian Mathematical Congress, Montreal. MR0275616
  • 107
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • B. Scholkopf, C. J. C. Burges and A. J. Smola, eds, MIT Press, Cambridge, MA
    • PLATT, J. (1999). Fast training of support vector machines using sequential minimal optimization. In Advances in Kernel Methods - Support Vector Learning (B. Scholkopf, C. J. C. Burges and A. J. Smola, eds.) 185-208. MIT Press, Cambridge, MA.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 185-208
    • PLATT, J.1
  • 108
    • 0016765357 scopus 로고
    • On optimal nonlinear associative recall
    • MR0503978
    • POGGIO, T. (1975). On optimal nonlinear associative recall. Biological Cybernetics 19 201-209. MR0503978
    • (1975) Biological Cybernetics , vol.19 , pp. 201-209
    • POGGIO, T.1
  • 109
    • 0025490985 scopus 로고
    • Networks for approximation and learning
    • POGGIO, T. and GIROSI, F. (1990). Networks for approximation and learning. Proceedings of the IEEE 78 1481-1497.
    • (1990) Proceedings of the IEEE , vol.78 , pp. 1481-1497
    • POGGIO, T.1    GIROSI, F.2
  • 113
    • 0005472817 scopus 로고
    • On measures of dependence
    • MR0115203
    • RÉNYI, A. (1959). On measures of dependence. Acta Math. Acad. Sci. Hungar. 10 441-451. MR0115203
    • (1959) Acta Math. Acad. Sci. Hungar , vol.10 , pp. 441-451
    • RÉNYI, A.1
  • 114
    • 51049093879 scopus 로고    scopus 로고
    • ROCKAFELLAR, R. T. (1970). Convex Analysis. Princeton Univ. Press. MR0274683
    • ROCKAFELLAR, R. T. (1970). Convex Analysis. Princeton Univ. Press. MR0274683
  • 115
    • 0001743201 scopus 로고
    • Metric spaces and completely monotone functions
    • MR1503439
    • SCHOENBERG, I. J. (1938). Metric spaces and completely monotone functions. Ann. Math. 39 811-841. MR1503439
    • (1938) Ann. Math , vol.39 , pp. 811-841
    • SCHOENBERG, I.J.1
  • 116
    • 0003893955 scopus 로고    scopus 로고
    • R. Oldenbourg Verlag, Munich. Available at
    • SCHÖLKOPF, B. (1997). Support Vector Learning. R. Oldenbourg Verlag, Munich. Available at http://www.kernel-machines.org.
    • (1997) Support Vector Learning
    • SCHÖLKOPF, B.1
  • 119
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear component analysis as a kernel eigenvalue problem
    • SCHÖLKOPF, B., SMOLA, A. J. and MÜLLER, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10 1299-1319.
    • (1998) Neural Comput , vol.10 , pp. 1299-1319
    • SCHÖLKOPF, B.1    SMOLA, A.J.2    MÜLLER, K.-R.3
  • 122
    • 85043116988 scopus 로고    scopus 로고
    • Shallow parsing with conditional random fields
    • Association for Computational Linguistics, Edmonton, Canada
    • SHA, F. and PEREIRA, F. (2003). Shallow parsing with conditional random fields. In Proceedings of HLT-NAACL 213-220. Association for Computational Linguistics, Edmonton, Canada.
    • (2003) Proceedings of HLT-NAACL , pp. 213-220
    • SHA, F.1    PEREIRA, F.2
  • 124
    • 51049100929 scopus 로고    scopus 로고
    • SMOLA, A. J., BARTLETT, P. L., SCHÖLKOPF, B. and SCHUURMANS, D. (2000). Advances in Large Margin Classifiers. MIT Press, Cambridge, MA. MR1820960
    • SMOLA, A. J., BARTLETT, P. L., SCHÖLKOPF, B. and SCHUURMANS, D. (2000). Advances in Large Margin Classifiers. MIT Press, Cambridge, MA. MR1820960
  • 125
    • 9444285502 scopus 로고    scopus 로고
    • Kernels and regularization on graphs
    • Proc. Annual Conf. Computational Learning Theory B. Schölkopf and M. K. Warmuth, eds, Springer, Heidelberg
    • SMOLA, A.J. and KONDOR, I. R. (2003). Kernels and regularization on graphs. Proc. Annual Conf. Computational Learning Theory (B. Schölkopf and M. K. Warmuth, eds.). Lecture Notes in Comput. Sci. 2726 .144-158. Springer, Heidelberg.
    • (2003) Lecture Notes in Comput. Sci , vol.2726 , pp. 144-158
    • SMOLA, A.J.1    KONDOR, I.R.2
  • 126
    • 24044515976 scopus 로고    scopus 로고
    • On a kernel-based method for pattern recognition, regression, approximation and operator inversion
    • MR1637511
    • SMOLA, A. J. and SCHÖLKOPF, B. (1998). On a kernel-based method for pattern recognition, regression, approximation and operator inversion. Algorithmica 22 211-231. MR1637511
    • (1998) Algorithmica , vol.22 , pp. 211-231
    • SMOLA, A.J.1    SCHÖLKOPF, B.2
  • 127
    • 0032098361 scopus 로고    scopus 로고
    • The connection between regularization operators and support vector kernels
    • SMOLA, A. J., SCHÖLKOPF, B. and MÜLLER, K.-R. (1998). The connection between regularization operators and support vector kernels. Neural Networks 11 637-649.
    • (1998) Neural Networks , vol.11 , pp. 637-649
    • SMOLA, A.J.1    SCHÖLKOPF, B.2    MÜLLER, K.-R.3
  • 128
    • 0010786475 scopus 로고    scopus 로고
    • On the influence of the kernel on the consistency of support vector machines
    • MR1883281
    • STEINWART, I. (2002). On the influence of the kernel on the consistency of support vector machines. J. Mach. Learn. Res. 2 67-93. MR1883281
    • (2002) J. Mach. Learn. Res , vol.2 , pp. 67-93
    • STEINWART, I.1
  • 129
    • 0036749277 scopus 로고    scopus 로고
    • Support vector machines are universally consistent
    • MR1928806
    • STEINWART, I. (2002). Support vector machines are universally consistent. J. Complexity 18 768-791. MR1928806
    • (2002) J. Complexity , vol.18 , pp. 768-791
    • STEINWART, I.1
  • 130
    • 0001558197 scopus 로고
    • Positive definite functions and generalizations, an historical survey
    • MR0430674
    • STEWART, J. (1976). Positive definite functions and generalizations, an historical survey. Rocky Mountain J. Math. 6 409-434. MR0430674
    • (1976) Rocky Mountain J. Math , vol.6 , pp. 409-434
    • STEWART, J.1
  • 131
    • 0002081773 scopus 로고    scopus 로고
    • Support vector regression with ANOVA decomposition kernels
    • B. Schölkopf, C. J. C. Burges and A. J. Smola, 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 Advances in Kernel Methods - Support Vector Learning (B. Schölkopf, C. J. C. Burges and A. J. Smola, eds.) 285-292. MIT Press, Cambridge, MA.
    • (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
  • 132
    • 84898948585 scopus 로고    scopus 로고
    • Max-margin Markov networks
    • S. Thrun, L. Saul and B. Schölkopf, eds, MIT Press, Cambridge, MA
    • TASKAR, B., GUESTRIN, C. and KOLLER, D. (2004). Max-margin Markov networks. In Aavances in Neural Information Processing Systems 16 (S. Thrun, L. Saul and B. Schölkopf, eds.) 25-32. MIT Press, Cambridge, MA.
    • (2004) Aavances in Neural Information Processing Systems , vol.16 , pp. 25-32
    • TASKAR, B.1    GUESTRIN, C.2    KOLLER, D.3
  • 134
    • 51049096203 scopus 로고    scopus 로고
    • TAX, D. M. J. and DUIN, R. P. W. (1999). Data domain description by support vectors. In Proceedings ESANN (M. Verleysen, ed.) 251-256. D Facto, Brussels.
    • TAX, D. M. J. and DUIN, R. P. W. (1999). Data domain description by support vectors. In Proceedings ESANN (M. Verleysen, ed.) 251-256. D Facto, Brussels.
  • 135
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • MR1379242
    • TIBSHIRANI, R. (1996). Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 58 267-288. MR1379242
    • (1996) J. R. Stat. Soc. Ser. B Stat. Methodol , vol.58 , pp. 267-288
    • TIBSHIRANI, R.1
  • 136
    • 0001300994 scopus 로고
    • Solution of incorrectly formulated problems and the regularization method
    • TIKHONOV, A. N. (1963). Solution of incorrectly formulated problems and the regularization method. Soviet Math. Dokl. 4 1035-1038.
    • (1963) Soviet Math. Dokl , vol.4 , pp. 1035-1038
    • TIKHONOV, A.N.1
  • 137
    • 24944537843 scopus 로고    scopus 로고
    • Large margin methods for structured and interdependent output variables
    • MR2249862
    • TSOCHANTARIDIS, I., JOACHIMS, T., HOFMANN, T. and ALTUN, Y. (2005). Large margin methods for structured and interdependent output variables. J. Mach. Learn. Res. 6 1453-1484. MR2249862
    • (2005) J. Mach. Learn. Res , vol.6 , pp. 1453-1484
    • TSOCHANTARIDIS, I.1    JOACHIMS, T.2    HOFMANN, T.3    ALTUN, Y.4
  • 139
    • 51049094982 scopus 로고    scopus 로고
    • VAPNIK, V. (1982). Estimation of Dependences Based on Empirical Data. Springer, Berlin. MR0672244
    • VAPNIK, V. (1982). Estimation of Dependences Based on Empirical Data. Springer, Berlin. MR0672244
  • 140
    • 51049122672 scopus 로고    scopus 로고
    • VAPNIK, V. (1995). The Nature of Statistical Learning Theory. Springer, New York. MR1367965
    • VAPNIK, V. (1995). The Nature of Statistical Learning Theory. Springer, New York. MR1367965
  • 141
    • 51049104919 scopus 로고    scopus 로고
    • VAPNIK, V. (1998). Statistical Learning Theory. Wiley, New York. MR1641250
    • VAPNIK, V. (1998). Statistical Learning Theory. Wiley, New York. MR1641250
  • 142
    • 0001024505 scopus 로고
    • On the uniform convergence of relative frequencies of events to their probabilities
    • VAPNIK, V. and CHERVONENKIS, A. (1971). On the uniform convergence of relative frequencies of events to their probabilities. Theory Probab. Appl. 16 264-281.
    • (1971) Theory Probab. Appl , vol.16 , pp. 264-281
    • VAPNIK, V.1    CHERVONENKIS, A.2
  • 143
    • 0000864140 scopus 로고
    • The necessary and sufficient conditions for consistency in the empirical risk minimization method
    • VAPNIK, V. and CHERVONENKIS, A. (1991). The necessary and sufficient conditions for consistency in the empirical risk minimization method. Pattern Recognition and Image Analysis 1 283-305.
    • (1991) Pattern Recognition and Image Analysis , vol.1 , pp. 283-305
    • VAPNIK, V.1    CHERVONENKIS, A.2
  • 144
    • 84887252594 scopus 로고    scopus 로고
    • Support vector method for function approximation, regression estimation, and signal processing
    • M. C. Mozer, M. I. Jordan and T. Petsche, eds, MIT Press, Cambridge, MA
    • VAPNIK, V., GOLOWICH, S. and SMOLA, A. J. (1997). Support vector method for function approximation, regression estimation, and signal processing. In Advances in Neural Information Processing Systems 9 (M. C. Mozer, M. I. Jordan and T. Petsche, eds.) 281-287. MIT Press, Cambridge, MA.
    • (1997) Advances in Neural Information Processing Systems , vol.9 , pp. 281-287
    • VAPNIK, V.1    GOLOWICH, S.2    SMOLA, A.J.3
  • 145
    • 0010864753 scopus 로고
    • Pattern recognition using generalized portrait method
    • VAPNIK, V. and LERNER, A. (1963). Pattern recognition using generalized portrait method. Autom. Remote Control 24 774-780.
    • (1963) Autom. Remote Control , vol.24 , pp. 774-780
    • VAPNIK, V.1    LERNER, A.2
  • 146
    • 33749236901 scopus 로고    scopus 로고
    • Fast kernels for string and tree matching
    • B. Schölkopf, K. Tsuda and J. P. Vert, eds, MIT Press, Cambridge, MA
    • VISHWANATHAN, S. V. N. and SMOLA, A. J. (2004). Fast kernels for string and tree matching. In Kernel Methods in Computational Biology (B. Schölkopf, K. Tsuda and J. P. Vert, eds.) 113-130. MIT Press, Cambridge, MA.
    • (2004) Kernel Methods in Computational Biology , pp. 113-130
    • VISHWANATHAN, S.V.N.1    SMOLA, A.J.2
  • 147
    • 33846637208 scopus 로고    scopus 로고
    • Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes
    • VISHWANATHAN, S. V. N., SMOLA, A. J. and VIDAL, R. (2007). Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes. Internat. J. Computer Vision 73 95-119.
    • (2007) Internat. J. Computer Vision , vol.73 , pp. 95-119
    • VISHWANATHAN, S.V.N.1    SMOLA, A.J.2    VIDAL, R.3
  • 148
    • 51049111283 scopus 로고    scopus 로고
    • WAHBA, G. (1990). Spline Models for Observational Data. SIAM, Philadelphia. MR1045442
    • WAHBA, G. (1990). Spline Models for Observational Data. SIAM, Philadelphia. MR1045442
  • 149
    • 51049094983 scopus 로고    scopus 로고
    • WAHBA, G., WANG, Y., GU, C., KLEIN, R. and KLEIN, B. (1995). Smoothing spline ANOVA for exponential families, with application to the Wisconsin Epidemiological Study of Diabetic Retinopathy. Ann. Statist. 23 1865-1895. MR1389856
    • WAHBA, G., WANG, Y., GU, C., KLEIN, R. and KLEIN, B. (1995). Smoothing spline ANOVA for exponential families, with application to the Wisconsin Epidemiological Study of Diabetic Retinopathy. Ann. Statist. 23 1865-1895. MR1389856
  • 150
    • 51049122464 scopus 로고    scopus 로고
    • WAINWRIGHT, M. J. and JORDAN, M. I. (2003). Graphical models, exponential families, and variational inference. Technical Report 649, Dept. Statistics, Univ. California, Berkeley.
    • WAINWRIGHT, M. J. and JORDAN, M. I. (2003). Graphical models, exponential families, and variational inference. Technical Report 649, Dept. Statistics, Univ. California, Berkeley.
  • 151
    • 51049104698 scopus 로고    scopus 로고
    • WATKLNS, C. (2000). Dynamic alignment kernels. In Advances in Large Margin Classifiers (A. J. Smola, P. L. Bartlett, B. Schölkopf and. D. Schuurmans, eds.) 39-50. MIT Press, Cambridge, MA. MR1820960
    • WATKLNS, C. (2000). Dynamic alignment kernels. In Advances in Large Margin Classifiers (A. J. Smola, P. L. Bartlett, B. Schölkopf and. D. Schuurmans, eds.) 39-50. MIT Press, Cambridge, MA. MR1820960
  • 152
    • 51049111285 scopus 로고    scopus 로고
    • WENDLAND, H. (2005). Scattered Data Approximation. Cambridge Univ. Press. MR2131724
    • WENDLAND, H. (2005). Scattered Data Approximation. Cambridge Univ. Press. MR2131724
  • 153
    • 84898971943 scopus 로고    scopus 로고
    • WESTON, J., CHAPELLE, O., ELISSEEFF, A., SCHÖLKOPF, B. and VAPNIK, V. (2003). Kernel dependency estimation. In Advances in Neural Information Processing Systems 15 (S. T. S. Becker and K. Obermayer, eds.) 873-880. MIT Press, Cambridge, MA. MR1820960
    • WESTON, J., CHAPELLE, O., ELISSEEFF, A., SCHÖLKOPF, B. and VAPNIK, V. (2003). Kernel dependency estimation. In Advances in Neural Information Processing Systems 15 (S. T. S. Becker and K. Obermayer, eds.) 873-880. MIT Press, Cambridge, MA. MR1820960
  • 154
    • 51049096426 scopus 로고    scopus 로고
    • WHITTAKER, J. (1990). Graphical Models in Applied Multivariate Statistics. Wiley, New York. MR1112133
    • WHITTAKER, J. (1990). Graphical Models in Applied Multivariate Statistics. Wiley, New York. MR1112133
  • 155
    • 0000056917 scopus 로고    scopus 로고
    • Adaptive on-line learning algorithms for blind separation - maximum entropy and minimum mutual information
    • YANG, H. H. and AMARI, S.-I. (1997). Adaptive on-line learning algorithms for blind separation - maximum entropy and minimum mutual information. Neural Comput. 9 1457-1482.
    • (1997) Neural Comput , vol.9 , pp. 1457-1482
    • YANG, H.H.1    AMARI, S.-I.2
  • 156
    • 36348934176 scopus 로고    scopus 로고
    • Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars
    • AUAI Press, Arlington, Virginia
    • ZETTLEMOYER, L. S. and COLLINS, M. (2005). Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In Uncertainty in Artificial Intelligence UAI 658-666. AUAI Press, Arlington, Virginia.
    • (2005) Uncertainty in Artificial Intelligence UAI , pp. 658-666
    • ZETTLEMOYER, L.S.1    COLLINS, M.2
  • 157
    • 0033670134 scopus 로고    scopus 로고
    • Engineering support vector machine kernels that recognize translation initiation sites
    • ZIEN, A., RATSCH, G., MIKA, S., SCHÖLKOPF, B., LENGAUER, T. and MÜLLER, K.-R. (2000). Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics 16 799-807.
    • (2000) Bioinformatics , vol.16 , pp. 799-807
    • ZIEN, A.1    RATSCH, G.2    MIKA, S.3    SCHÖLKOPF, B.4    LENGAUER, T.5    MÜLLER, K.-R.6


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