메뉴 건너뛰기




Volumn , Issue , 2006, Pages 253-282

Kernel Methods for Graphs

Author keywords

Kernel methods for graphs; Supervised learning problem; Vertex classification

Indexed keywords


EID: 77954874229     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470073049.ch11     Document Type: Chapter
Times cited : (5)

References (41)
  • 2
    • 0002935122 scopus 로고    scopus 로고
    • Combining support vector and mathematical programming methods for classification
    • In Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge
    • K. Bennett. Combining support vector and mathematical programming methods for classification. In Advances in Kernel Methods-Support Vector Learning, pp. 307-326, MIT Press, Cambridge, 1998.
    • (1998) , pp. 307-326
    • Bennett, K.1
  • 3
    • 85037918238 scopus 로고
    • A tourist guide through treewidth
    • H. L. Bodlaender. A tourist guide through treewidth. Acta Cybern., 11(1-2):1-22, 1993.
    • (1993) Acta Cybern. , vol.11 , Issue.1-2 , pp. 1-22
    • Bodlaender, H.L.1
  • 4
    • 0002981945 scopus 로고    scopus 로고
    • A partial k-arboretum of graphs with bounded treewidth
    • H. L. Bodlaender. A partial k-arboretum of graphs with bounded treewidth. Theoretical Computer Science, 209(1-2):1-45, 1998.
    • (1998) Theoretical Computer Science , vol.209 , Issue.1-2 , pp. 1-45
    • Bodlaender, H.L.1
  • 5
    • 2442483205 scopus 로고    scopus 로고
    • Mining molecular fragments: Finding relevant substructures of molecules
    • In Proceedings of the 2002 IEEE International Conference on Data Mining. IEEE Computer Society
    • C. Borgelt and M. R. Berthold. Mining molecular fragments: Finding relevant substructures of molecules. In Proceedings of the 2002 IEEE International Conference on Data Mining. IEEE Computer Society, 2002.
    • (2002)
    • Borgelt, C.1    Berthold, M.R.2
  • 6
    • 0026966646 scopus 로고
    • A training algorithm for optimal margin classifiers
    • In D. Haussler, ed. Proceedings of the Annual Conference on Computational Learning Theory, ACM Press, New York
    • B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, ed. Proceedings of the Annual Conference on Computational Learning Theory, pp. 144-152, ACM Press, New York, 1992.
    • (1992) , pp. 144-152
    • Boser, B.E.1    Guyon, I.M.2    Vapnik, V.N.3
  • 7
    • 27944478812 scopus 로고    scopus 로고
    • Automated approaches for classifying structures
    • In Proceedings of the 2nd ACM SIGKDD Workshop on Data Mining and Bioinformatics
    • M. Deshpande, M. Kuramochi, and G. Karypis. Automated approaches for classifying structures. In Proceedings of the 2nd ACM SIGKDD Workshop on Data Mining and Bioinformatics, 2002.
    • (2002)
    • Deshpande, M.1    Kuramochi, M.2    Karypis, G.3
  • 8
    • 34547984408 scopus 로고    scopus 로고
    • Frequent sub-structure based approaches for classifying chemical compounds
    • In Proceedings of the 3rd IEEE International Conference on Data Mining, IEEE Computer Society
    • M. Deshpande, M. Kuramochi, and G. Karypis. Frequent sub-structure based approaches for classifying chemical compounds. In Proceedings of the 3rd IEEE International Conference on Data Mining, pp. 35-42. IEEE Computer Society, 2003.
    • (2003) , pp. 35-42
    • Deshpande, M.1    Kuramochi, M.2    Karypis, G.3
  • 9
    • 0003768769 scopus 로고
    • Practical Methods of Optimization
    • Wiley, New York
    • R. Fletcher. Practical Methods of Optimization. Wiley, New York, 1989.
    • (1989)
    • Fletcher, R.1
  • 10
    • 4544242894 scopus 로고    scopus 로고
    • The parameterized complexity of counting problems
    • J. Flum and M. Grohe. The parameterized complexity of counting problems. SIAM Journal on Computing, 33(4):892-922, 2004.
    • (2004) SIAM Journal on Computing , vol.33 , Issue.4 , pp. 892-922
    • Flum, J.1    Grohe, M.2
  • 11
    • 4444231365 scopus 로고    scopus 로고
    • A survey of kernels for structured data
    • July
    • T. Gärtner. A survey of kernels for structured data. SIGKDD Explorations, 5(1):49-58, July 2003.
    • (2003) SIGKDD Explorations , vol.5 , Issue.1 , pp. 49-58
    • Gärtner, T.1
  • 12
    • 84889358933 scopus 로고    scopus 로고
    • Predictive graph mining with kernel methods
    • In Advanced Methods for Knowledge Discovery from Complex Data. Springer, Berlin
    • T. Gärtner. Predictive graph mining with kernel methods. In Advanced Methods for Knowledge Discovery from Complex Data. Springer, Berlin, 2005.
    • (2005)
    • Gärtner, T.1
  • 13
    • 23844510632 scopus 로고    scopus 로고
    • On graph kernels: Hardness results and efficient alternatives
    • In Proceedings of the 16th Annual Conference on Computational Learning Theory and the 7th Kernel Workshop
    • T. Gärtner, P. A. Flach, and S. Wrobel. On graph kernels: Hardness results and efficient alternatives. In Proceedings of the 16th Annual Conference on Computational Learning Theory and the 7th Kernel Workshop, 2003.
    • (2003)
    • Gärtner, T.1    Flach, P.A.2    Wrobel, S.3
  • 15
    • 84871508412 scopus 로고    scopus 로고
    • Relevant cycles in biopolymers and random graph
    • In Proceedings of the 4th Slovene International Conference in Graph Theory
    • P. M. Gleiss and P. F. Stadler. Relevant cycles in biopolymers and random graph. In Proceedings of the 4th Slovene International Conference in Graph Theory, 1999.
    • (1999)
    • Gleiss, P.M.1    Stadler, P.F.2
  • 16
    • 12244294297 scopus 로고    scopus 로고
    • PAC-Bayesian pattern classification with kernels
    • Ph.D. thesis, TU Berlin
    • T. Graepel. PAC-Bayesian pattern classification with kernels. Ph.D. thesis, TU Berlin, 2002.
    • (2002)
    • Graepel, T.1
  • 17
  • 18
    • 26944479661 scopus 로고    scopus 로고
    • Cyclic pattern kernels revisited
    • In Proceedings of Advances in Knowledge Discovery and Data Mining, 9th Pacific-Asia Conference, PAKDD 2005, Vol. 3518 of LNAI, Springer, Berlin
    • T. Horváth. Cyclic pattern kernels revisited. In Proceedings of Advances in Knowledge Discovery and Data Mining, 9th Pacific-Asia Conference, PAKDD 2005, Vol. 3518 of LNAI, pp. 791-801. Springer, Berlin, 2005.
    • (2005) , pp. 791-801
    • Horváth, T.1
  • 19
    • 12244278576 scopus 로고    scopus 로고
    • Cyclic pattern kernels for predictive graph mining
    • In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    • T. Horváth, T. Gärtner, and S. Wrobel. Cyclic pattern kernels for predictive graph mining. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 158-167, 2004.
    • (2004) , pp. 158-167
    • Horváth, T.1    Gärtner, T.2    Wrobel, S.3
  • 20
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale SVM learning practical
    • In B. Schölkopf, C. J. C. Burges, and A. J. Smola, eds. Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, MA
    • T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, eds. Advances in Kernel Methods-Support Vector Learning, pp. 169-184, MIT Press, Cambridge, MA, 1999.
    • (1999) , pp. 169-184
    • Joachims, T.1
  • 21
    • 0038167128 scopus 로고    scopus 로고
    • Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms
    • The Kluwer International Series in Engineering and Computer Science. Kluwer Academic, Boston
    • T. Joachims. Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms. The Kluwer International Series in Engineering and Computer Science. Kluwer Academic, Boston, 2002.
    • (2002)
    • Joachims, T.1
  • 22
    • 0033225865 scopus 로고    scopus 로고
    • An introduction to variational methods for graphical models
    • M. I. Jordan, Z. Ghahramani, Tommi S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. Machine Learning, 37(2):183-233, 1999.
    • (1999) Machine Learning , vol.37 , Issue.2 , pp. 183-233
    • Jordan, M.I.1    Ghahramani, Z.2    Jaakkola, T.S.3    Saul, L.K.4
  • 23
    • 84898960990 scopus 로고    scopus 로고
    • Learning semantic similarity
    • In Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA
    • J. Kandola, J. Shawe-Taylor, and N. Cristianini. Learning semantic similarity. In Advances in Neural Information Processing Systems, Vol. 15. MIT Press, Cambridge, MA, 2003.
    • (2003) , vol.15
    • Kandola, J.1    Shawe-Taylor, J.2    Cristianini, N.3
  • 24
    • 0041775676 scopus 로고    scopus 로고
    • Diffusion kernels on graphs and other discrete structures
    • In Proceedings of the 19th International Conference on Machine Learning
    • I. R. Kondor and J. D. Lafferty. Diffusion kernels on graphs and other discrete structures. In Proceedings of the 19th International Conference on Machine Learning, pp. 315-312, 2002.
    • (2002) , pp. 315-312
    • Kondor, I.R.1    Lafferty, J.D.2
  • 25
    • 0035789622 scopus 로고    scopus 로고
    • Molecular feature mining in HIV data
    • In Proceedings and the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    • S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in HIV data. In Proceedings and the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 136-143, 2002.
    • (2002) , pp. 136-143
    • Kramer, S.1    De Raedt, L.2    Helma, C.3
  • 26
    • 0004047518 scopus 로고    scopus 로고
    • Graphical Models
    • Oxford University Press, Oxford
    • S. L. Lauritzen. Graphical Models. Oxford University Press, Oxford, 1996.
    • (1996)
    • Lauritzen, S.L.1
  • 27
    • 4444241046 scopus 로고    scopus 로고
    • Logic for Learning
    • Springer, Berlin
    • J. W. Lloyd. Logic for Learning. Springer, Berlin, 2003.
    • (2003)
    • Lloyd, J.W.1
  • 28
    • 0012035459 scopus 로고
    • Mathematical basis of ring-finding algorithms at CIDS
    • M. Plotkin. Mathematical basis of ring-finding algorithms at CIDS. J. Chem. Doc., 11:60-63, 1971.
    • (1971) J. Chem. Doc. , vol.11 , pp. 60-63
    • Plotkin, M.1
  • 29
    • 0016523213 scopus 로고
    • Bounds on backtrack algorithms for listing cycles, paths, and spanning trees
    • R. C. Read and R. E. Tarjan. Bounds on backtrack algorithms for listing cycles, paths, and spanning trees. Networks, 5(3):237-252, 1975.
    • (1975) Networks , vol.5 , Issue.3 , pp. 237-252
    • Read, R.C.1    Tarjan, R.E.2
  • 30
    • 0242456823 scopus 로고    scopus 로고
    • Mining knowledge-sharing sites for viral marketing
    • In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    • M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002.
    • (2002)
    • Richardson, M.1    Domingos, P.2
  • 31
    • 0000673493 scopus 로고
    • Graph minors. II. Algorithmic aspects of tree-width
    • N. Robertson and P. D. Seymour. Graph minors. II. Algorithmic aspects of tree-width. Journal of Algorithms, 7(3):309-322, 1986.
    • (1986) Journal of Algorithms , vol.7 , Issue.3 , pp. 309-322
    • Robertson, N.1    Seymour, P.D.2
  • 32
    • 0004094721 scopus 로고    scopus 로고
    • Learning with Kernels
    • MIT Press, Cambridge, MA
    • B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.
    • (2002)
    • Schölkopf, B.1    Smola, A.J.2
  • 33
    • 9444285502 scopus 로고    scopus 로고
    • Kernels and regularization on graphs
    • In B. Schölkopf and M. K. Warmuth, eds. Proceedings of the Annual Conference on Computational Learning Theory, Lecture Notes in Computer Science. Springer, Berlin
    • A. J. Smola and I. R. Kondor. Kernels and regularization on graphs. In B. Schölkopf and M. K. Warmuth, eds. Proceedings of the Annual Conference on Computational Learning Theory, Lecture Notes in Computer Science. Springer, Berlin, 2003.
    • (2003)
    • Smola, A.J.1    Kondor, I.R.2
  • 34
    • 0001790593 scopus 로고
    • Depth-first search and linear graph algorithms
    • R. Tarjan. Depth-first search and linear graph algorithms. SIAM Journal on Computing, 1(2):146-160, 1972.
    • (1972) SIAM Journal on Computing , vol.1 , Issue.2 , pp. 146-160
    • Tarjan, R.1
  • 35
    • 0003991806 scopus 로고    scopus 로고
    • Statistical Learning Theory
    • Wiley, New York
    • V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.
    • (1998)
    • Vapnik, V.1
  • 36
    • 33749236901 scopus 로고    scopus 로고
    • Fast kernels for string and tree matching
    • In K. Tsuda, B. Schölkopf, and J.P. Vert, eds. Kernels and Bioinformatics, MIT Press, Cambridge, MA
    • S. V. N. Vishwanathan and A. J. Smola. Fast kernels for string and tree matching. In K. Tsuda, B. Schölkopf, and J.P. Vert, eds. Kernels and Bioinformatics, MIT Press, Cambridge, MA, 2004.
    • (2004)
    • Vishwanathan, S.V.N.1    Smola, A.J.2
  • 37
    • 13744260090 scopus 로고    scopus 로고
    • Union of all the minimum cycle bases of a graph
    • P. Vismara. Union of all the minimum cycle bases of a graph. Electronic Journal of Combinatorics, 4(1):73-87, 1997.
    • (1997) Electronic Journal of Combinatorics , vol.4 , Issue.1 , pp. 73-87
    • Vismara, P.1
  • 39
    • 0242709382 scopus 로고    scopus 로고
    • Efficiently mining frequent trees in a forest
    • In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York
    • M. Zaki. Efficiently mining frequent trees in a forest. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71-80, ACM Press, New York, 2002.
    • (2002) , pp. 71-80
    • Zaki, M.1
  • 40
    • 31844438615 scopus 로고    scopus 로고
    • Learning from labeled and unlabeled data on a directed graph
    • In International Conference on Machine Learning
    • D. Zhou, J. Huang, and B. Schölkopf. Learning from labeled and unlabeled data on a directed graph. In International Conference on Machine Learning, 2005.
    • (2005)
    • Zhou, D.1    Huang, J.2    Schölkopf, B.3
  • 41
    • 48349094482 scopus 로고    scopus 로고
    • Semi-supervised learning using gaussian fields and harmonic functions
    • In International Conference on Machine Learning ICML'03
    • X. Zhu, J. Lafferty, and Z. Ghahramani. Semi-supervised learning using gaussian fields and harmonic functions. In International Conference on Machine Learning ICML'03, 2003.
    • (2003)
    • Zhu, X.1    Lafferty, J.2    Ghahramani, Z.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.