2003 ASurveyOfKernelsForStructuredData

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Subject Headings: Kernel-based Algorithm, Graph Mining, Kernel Methods, structured data, multi-relational data mining, inductive logic programming.

Notes

Cited By

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Quotes

Abstract

Kernel methods in general and support vector machines in particular have been successful in various learning tasks on data represented in a single table . Much 'real-world' data, however, is structured - it has no natural representation in a single table. Usually, to apply kernel methods to 'real-world' data, extensive pre-processing is performed to embed the data into a real vector space and thus in a single table. This survey describes several approaches of defining positive definite kernels on structured instances directly.

References

  • N. Aronszajn. Theory of reproducing kernels. Transactions of the American Mathematical Society, 68, 1950.
  • Kristin P. Bennett, Colin Campbell, Support vector machines: hype or hallelujah?, ACM SIGKDD Explorations Newsletter, v.2 n.2, p.1-13, Dec. 2000 doi:10.1145/380995.380999
  • Bernhard E. Boser, Isabelle M. Guyon, Vladimir N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, p.144-152, July 27-29, 1992, Pittsburgh, Pennsylvania, United States doi:10.1145/130385.130401
  • Michael Collins and N. Duffy. Convolution kernels for natural language. In Thomas G. Dietterich, S. Becker, and Zoubin Ghahramani, editors, Advances in Neural Information Processing Systems, volume 14. MIT Press, 2002.
  • Nello Cristianini, John Shawe-Taylor, An introduction to support Vector Machines: and other kernel-based learning methods, Cambridge University Press, New York, NY, 1999
  • Thomas G. Dietterich, Richard H. Lathrop, Tomás Lozano-Pérez, Solving the multiple instance problem with axis-parallel rectangles, Artificial Intelligence, v.89 n.1-2, p.31-71, Jan. 1997 doi:10.1016/S0004-3702(96)00034-3
  • R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998.
  • Saso (Ed.) Dzeroski, Nada Lavrac, Saso Dzeroski, Relational Data Mining, Springer-Verlag New York, Inc., Secaucus, NJ, 2001
  • S. Džeroski, S. Schulze-Kremer, K. Heidtke, K. Siems, D. Wettschereck, and H. Blockeel. Diterpene structure elucidation from 13C NMR spectra with inductive logic programming. Applied Artificial Intelligence, 12(5):363--383, July-Aug. (1998). Special Issue on First-Order Knowledge Discovery in Databases.
  • T. Gärtner. Exponential and geometric kernels for graphs. In NIPS Workshop on Unreal Data: Principles of Modeling Nonvectorial Data, 2002.
  • T. Gärtner, K. Driessens, and J. Ramon. Graph kernels and gaussian processes for relational reinforcement learning. In: Proceedings of the 13th International Conference on Inductive Logic Programming, 2003.
  • Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alex J. Smola, Multi-Instance Kernels, Proceedings of the Nineteenth International Conference on Machine Learning, p.179-186, July 08-12, 2002
  • T. Gärtner, P. A. Flach, and S. Wrobel. On graph kernels: Hardness results and efficient alternatives. In: Proceedings of the 16th Annual Conference on Computa tional Learning Theory and the 7th Kernel Workshop, 2003.
  • T. Gärtner, J. W. Lloyd, and P. A. Flach. Kernels for structured data. In: Proceedings of the 12th International Conference on Inductive Logic Programming. Springer-Verlag, 2002.
  • D. Haussler. Convolution kernels on discrete structures. Technical report, Department of Computer Science, University of California at Santa Cruz, 1999.
  • T. Jaakkola, M. Diekhans, and D. Haussler. A discriminative framework for detecting remote protein homologies. Journal of Computational Biology, 7(1, 2), 2000.
  • Tommi S. Jaakkola, David Haussler, Exploiting generative models in discriminative classifiers, Proceedings of the 1998 conference on Advances in Neural Information Processing Systems II, p.487-493, July 1999
  • T. Jaakkola and D. Haussler. Probabilistic kernel regression models. In: Proceedings of the 1999 Conference on AI and Statistics, 1999.
  • Thorsten Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms, Kluwer Academic Publishers, Norwell, MA, 2002
  • R. Karchin, K. Karplus, and D. Haussler. Classifying g-protein coupled receptors with support vector machines. Bioinformatics, 18(1):147--159, 2002.
  • H. Kashima and A. Inokuchi. Kernels for graph classification. In ICDM Workshop on Active Mining, 2002.
  • Hisashi Kashima, Teruo Koyanagi, Kernels for Semi-Structured Data, Proceedings of the Nineteenth International Conference on Machine Learning, p.291-298, July 08-12, 2002
  • H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between labeled graphs. In: Proceedings of the 20th International Conference on Machine Learning, 2003.
  • Risi Imre Kondor, John D. Lafferty, Diffusion Kernels on Graphs and Other Discrete Input Spaces, Proceedings of the Nineteenth International Conference on Machine Learning, p.315-322, July 08-12, 2002
  • Stefan Kramer, Nada Lavrač, Peter Flach, Propositionalization approaches to relational data mining, Relational Data Mining, Springer-Verlag New York, Inc., New York, NY, 2001
  • Mark A. Krogel, Stefan Wrobel, Transformation-based Learning Using Multirelational Aggregation, Proceedings of the 11th International Conference on Inductive Logic Programming, p.142-155, September 09-11, 2001
  • C. Leslie, E. Eskin, andW. Noble. The spectrum kernel: A string kernel for svm protein classification. In: Proceedings of the Pacific Symposium on Biocomputing, pages 564--575, 2002.
  • C. Leslie, E. Eskin, J. Weston, and W. Noble. Mismatch string kernels for svm protein classification. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems, volume 15. MIT Press, 2003.
  • J. W. Lloyd. Logic for Learning. Springer-Verlag, 2002.
  • Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Chris Watkins, Text classification using string kernels, The Journal of Machine Learning Research, 2, p.419-444, 3/1/2002
  • H. Lodhi, John Shawe-Taylor, N. Christianini, and C. Watkins. Text classification using string kernels. In T. Leen, Thomas G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems, volume 13. MIT Press, 2001.
  • K.R. Müller, S. Mika, G. Rätsch, K. Tsuda, and Bernhard Schölkopf. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 2(2), 2001.
  • Gerhard Paass, Edda Leopold, Martha Larson, Jörg Kindermann, Stefan Eickeler, SVM Classification Using Sequences of Phonemes and Syllables, Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery, p.373-384, August 19-23, 2002
  • P. Pavlidis, T. Furey, M. Liberto, D. Haussler, and W. Grundy. Promoter region-based classification of genes. In: Proceedings of the Pacific Symposium on Biocomputing, pages 151--163, 2001.
  • L. R. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257--285, Feb. 1989.
  • C. Saunders, John Shawe-Taylor, and A. Vinokourov. String kernels, fisher kernels and finite state automata. In S. Becker, S. * Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems, volume 15. MIT Press, 2003.
  • Bernhard Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, 2002.
  • N. Smith and M. Gales. Speech recognition using SVMs. In Thomas G. Dietterich, S. Becker, and Zoubin Ghahramani, editors, Advances in Neural Information Processing Systems, volume 14. MIT Press, 2002.
  • K. Tsuda, M. Kawanabe, G. Rätsch, S. Sonnenburg, and K.-R. Müller. A new discriminative kernel from probabilistic models. In Thomas G. Dietterich, S. Becker, and Zoubin Ghahramani, editors, Advances in Neural Information Processing Systems, volume 14. MIT Press, 2002.
  • K. Tsuda, T. Kin, and K. Asai. Marginalized kernels for biological sequences. Bioinformatics, 2002.
  • Vladimir N. Vapnik, The nature of statistical learning theory, Springer-Verlag New York, Inc., New York, NY, 1995
  • J.P. Vert. A tree kernel to analyze phylogenetic profiles. Bioinformatics, 2002.
  • J.P. Vert and M. Kanehisa. Graph driven features extraction from microarray data using diffusion kernels and kernel cca. In S. Becker, S. Thrun, and K. Ober mayer, editors, Advances in Neural Information Processing Systems, volume 15. MIT Press, 2003.
  • S. Vishwanathan and Alexander J. Smola. Fast kernels for string and tree matching. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems, volume 15. MIT Press, 2003.
  • C. Watkins. Dynamic alignment kernels. Technical report, Department of Computer Science, Royal Holloway, University of London, 1999.
  • C. Watkins. Kernels from matching operations. Technical report, Department of Computer Science, Royal Holloway, University of London, 1999.
  • A, Zien, G. Ratsch, S. Mika, Bernhard Schölkopf, T. Lengauer, and K.-R. Muller. Engineering support vector machine kernels that recognize translation initiation sites. Bioinforrnatics, 16(9):799--807, (2000).

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2003 ASurveyOfKernelsForStructuredDataThomas GärtnerA Survey of Kernels for Structured DataACM SIGKDD Explorations Newsletterhttp://dx.doi.org/10.1145/959242.95924810.1145/959242.9592482003
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