Kernels for structured data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2583, Issue , 2003, Pages 66-83

  Gärtner, Thomas ^a   Lloyd, John W ^b   Flach, Peter A ^c  

^a FRAUNHOFER IAIS   (Germany)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTERS;

ATTRIBUTE VALUES; KERNEL METHODS; POSITIVE DEFINITE; STRUCTURED DATA;

INDUCTIVE LOGIC PROGRAMMING (ILP);

EID: 7044227547     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36468-4_5     Document Type: Conference Paper

References (16)

1. A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125-137, Dec. 2001.
2. E. Bloedorn, R. Michalski, and J. Wnek. Matching methods with problems: A comparative analysis of constructive induction approaches. Technical report, Machine Learning and Inference Laboratory, MLI 94-2, George Mason University, Fairfax, VA., 1994.
3. B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, editor, Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pages 144-152, Pittsburgh, PA, July 1992. ACM Press.
4. A. Church. A formulation of the simple theory of types. Journal of Symbolic Logic, 5:56-68, 1940.
5. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines (and Other Kernel-Based Learning Methods). Cambridge University Press, 2000.
6. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1-2):31-71, 1997.
7. T. Gärtner. Kernel-based feature space transformation in inductive logic programming. Master's thesis, University of Bristol, 2000.
8. T. Gärtner, P. A. Flach, A. Kowalczyk, and A. J. Smola. Multi-instance kernels. In C. Sammut and A. Hoffmann, editors, Proceedings of the 19th International Conference on Machine Learning, pages 179-186. Morgan Kaufmann, June 2002.
9. D. Haussler. Convolution kernels on discrete structures. Technical report, Department of Computer Science, University of California at Santa Cruz, 1999.
10. T. Horváth, S. Wrobel, and U. Bohnebeck. Relational instance-based learning with lists and terms. Machine Learning, 43(1/2):53-80, Apr. 2001.
11. S. P. Jones and J. Hughes, editors. Haskell98: A Non-Strict Purely Functional Language. Available at http://haskell.org/.
12. J. Lloyd. Knowledge representation, computation, and learning in higher-order logic. Available at http://csl.anu.edu.au/-jwl, 2001.
13. D. Michie, S. Muggleton, D. Page, and A. Srinivasan. To the international computing community: A new eastwest challenge. Technical report, Oxford University Computing laboratory, Oxford, UK, 1994.
14. J. Ramon and M. Bruynooghe. A polynomial time computable metric between point sets. Acta Informatica, 37(10):765-780, 2001.
15. B. Schölkopf and A. J. Smola. Learning with Kernels. The MIT Press, 2002.
16. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java implementations. Morgan Kaufmann, 2000.