Fast kernels for string and tree matching

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  Vishwanathan, S V N ^a   Smola, Alexander J ^b  

^a INDIAN INSTITUTE OF SCIENCE   (India)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FORESTRY;

DISCRETE OBJECTS; LINEAR TIME; QUADRATIC TIME; STRING KERNEL; SUPPORT VECTOR; TREE-MATCHING;

TREES (MATHEMATICS);

EID: 84898962121     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. A. Amir, M. Farach, Z. Galil, R. Giancarlo, and K. Park. Dynamic dictionary matching. Journal of Computer and System Science, 49(2): 208-222, October 1994.
2. W. I. Chang and E. L. Lawler. Sublinear approximate sting matching and biological applications. Algorithmica, 12(4/5): 327-344, 1994.
3. M. Collins and N. Duffy. Convolution kernels for natural language. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2001. MIT Press.
4. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press, 1998.
5. R. Giegerich and S. Kurtz. From Ukkonen to McCreight and Weiner: A unifying view of linear-time suffix tree construction. Algorithmica, 19(3): 331-353, 1997.
6. M. Gribskov and N. L. Robinson. Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Computers and Chemistry, 20(1): 25-33, 1996.
7. D. Haussler. Convolutional kernels on discrete structures. Technical Report UCSC-CRL-99-10, Computer Science Department, UC Santa Cruz, 1999.
8. R. Herbrich. Learning Kernel Classifiers: Theory and Algorithms. MIT Press, 2002.
9. T. S. Jaakkola, M. Diekhans, and D. Haussier. A discriminative framework for detecting remote protein homologies. Journal of Computational Biology, 7: 95-114, 2000.
10. T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 169-184, Cambridge, MA, 1999. MIT Press.
11. E. Leopold and J. Kindermann. Text categorization with support vector machines: How to represent text in input space? Machine Learning, 46(3): 423-444, March 2002.
12. C. Leslie, E. Eskin, and W. S. Noble. The spectrum kernel: A string kernel for SVM protein classification. In Proceedings of the Pacific Symposium on Biocomputing, pages 564-575, 2002.
13. E. Ukkonen. On-line construction of suffix trees. Algorithmica, 14(3): 249-260, 1995.
14. S. V. N. Vishwanathan. Kernel Methods: Fast Algorithms and Real Life Applications. PhD thesis, Indian Institute of Science, Bangalore, India, November 2002.
15. C. Watkins. Dynamic alignment kernels. In A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 39-50, Cambridge, MA, 2000. MIT Press.