Efficient computations of gapped string kernels based on suffix kernel

Neurocomputing

Volumn 71, Issue 4-6, 2008, Pages 944-962

  Yin, Chuanhuan ^a   Tian, Shengfeng ^a   Mu, Shaomin ^a   Shao, Chong ^b  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b PEOPLE S PUBLIC SECURITY UNIVERSITY OF CHINA   (China)

Author keywords

Bit parallel technique; Gapped string kernels; Kernel methods; String kernels; Suffix kernel

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTATIONAL EFFICIENCY; DYNAMIC PROGRAMMING; PARALLEL ALGORITHMS;

ALL-LENGTH MATCH-WEIGHTED KERNEL; BIT-PARALLEL TECHNIQUE; GAPPED STRING KERNELS; SUFFIX KERNEL TECHNIQUE;

REGRESSION ANALYSIS;

ALGORITHM; ARTICLE; CALCULATION; CONTROLLED STUDY; KERNEL METHOD; MATHEMATICAL COMPUTING; PRIORITY JOURNAL;

EID: 38649097589     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.02.005     Document Type: Article

References (23)

1. Agarwal P., and Erickson J. Geometric range searching and its relatives. Advances in Discrete and Computational Geometry. Contemporary Mathematics 23 (1999) 1-56
2. de Berg M., van Kreveld M., Overmars M., and Schwarzkopf O. Computational Geometry-Algorithms and Applications (1997), Springer, Berlin
3. Campbell C. Kernel methods: a survey of current techniques. Neurocomputing 48 (2002) 63-84
4. Cancedda N., Gaussier E., Goutte C., and Renders J.-M. Word-sequence kernels. Journal of Machine Learning Research 3 (2003) 1059-1082
5. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines (2000), Cambridge University Press, Cambridge, UK
6. S. Forrest, S.A. Hofmeyr, A. Somajayi, A sense of self for UNIX processes, in: Proceedings of IEEE Symposium on Computer Security and Privacy, 1996.
7. Haussler D. Convolution kernels on discrete structures, Technical Report UCSC-CRL-99-10 (1999), Department of Computer Science, University of California at Santa Cruz, Santa Cruz, CA
8. Hyyrö H., and Navarro G. Bit-parallel witnesses and their applications to approximate string matching. Algorithmic 41 (2004) 203-231
9. Joachims T. Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms (2002), Kluwer Academic Publishers, Norwell, MA
10. C. Leslie, E. Eskin, W.S. Noble, The spectrum kernel: a string kernel for SVM protein classification, in: Proceedings of the Pacific Symposium on Biocomputing, 2002, pp. 564-575.
11. Leslie C., Eskin E., Weston J., and Noble W.S. Mismatch string kernels for SVM protein classification. Advances in Neural Information Processing Systems 15 (2003) 1417-1424
12. Leslie C., and Kuang R. Fast string kernels using inexact matching for protein sequences. Journal of Machine Learning Research 5 (2004) 1435-1455
13. Lodhi H., Saunders C., Shawe-Taylor J., Cristianini N., and Watkins C. Text classification using string kernels. Journal of Machine Learning Research 2 (2002) 419-444
14. Myers G. A fast bit-vector algorithm for approximate string matching based on dynamic programming. Journal of the ACM 3 (1999) 395-415
15. Rousu J., and Shawe-Taylor J. Efficient computation of gapped substring kernels on large alphabets. Journal of Machine Learning Research 6 (2005) 1323-1344
16. Saigo H., Vert J.-P., Uedal N., and Akutsul T. Protein homology detection using string alignment kernels. Bioinformatics (2004)
17. Sánchez V.D. Advanced support vector machines and kernel methods. Neurocomputing 55 (2003) 5-20
18. Shawe-Taylor J., and Cristianini N. Kernel Methods for Pattern Analysis (2004), Cambridge University Press, Cambridge, UK
19. S.F. Tian, S.M. Mu, C.H. Yin, Anomaly detection using support vector machines, in: Proceedings of Advances in Neural Networks, Lecture Notes in Computer Science, vol. 3173, 2004, pp. 592-597.
20. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer, New York
21. Vishwanathan S., and Smola A. Fast kernels for string and tree matching. Advances in Neural Information Processing Systems 15 (2003) 569-576
22. Watkins C. Dynamic alignment kernels, Technical Report CSD-TR-98-11 (1999), Department of Computer Science, Royal Holloway University of London
23. C.H. Yin, S.F. Tian, S.M. Mu, Using gap-insensitive string kernel to detect masquerading, in: Proceedings of Advanced Data Mining and Applications, Lecture Notes in Artificial Intelligence, vol. 3584, 2005, pp. 323-330.