Efficient algorithms for similarity measures over sequential data: A look beyond kernels

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

Volumn 4174 LNCS, Issue , 2006, Pages 374-383

  Rieck, Konrad ^a   Laskov, Pavel ^a   Müller, Klaus Robert ^a,b  

^a FRAUNHOFER FIRST   (Germany)

^b UNIVERSITY OF POTSDAM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATION THEORY; COMPUTER NETWORKS; FUNCTIONS; MEASUREMENT THEORY;

KERNEL FUNCTIONS; SEQUENTIAL DATA;

DATA STRUCTURES;

EID: 33750258120     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11861898_38     Document Type: Conference Paper

References (22)

1. Hamming, R.W.: Error-detecting and error-correcting codes. Bell System Technical Journal 29(2) (1950) 147-160
2. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Doklady Akademii Nauk SSSR 163(4) (1964) 845-848
3. Salton, G.: Mathematics and information retrieval. Journal of Documentation 35(1) (1979) 1-29
4. Damashek, M.: Gauging similarity with n-grams: Language-independent categorization of text. Science 267(5199) (1995) 843-848
5. Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. Technical Report 23, LS VIII, University of Dortmund (1997)
6. Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.: A geometric framework for unsupervised anomaly detection: detecting intrusions in unlabeled data. In: Applications of Data Mining in Computer Security. Kluwer (2002)
7. Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lengauer, T., Müller, K.R.: Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites. BioInformatics 16(9) (2000) 799-807.
8. Leslie, C., Eskin, E., Noble, W.: The spectrum kernel: A string kernel for SVM protein classification. In: Proc. Pacific Symp. Biocomputing. (2002) 564-575
9. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text classification using string kernels. Journal of Machine Learning Research 2 (2002) 419-444
10. Vishwanathan, S., Smola, A.: Fast Kernels for String and Tree Matching. In: Kernels and Bioinformatics. MIT Press (2004) 113-130
11. Leslie, C., Eskin, E., Cohen, A., Weston, J., Noble, W.: Mismatch string kernel for discriminative protein classification. Bioinformatics 1(1) (2003) 1-10
12. Leslie, C., Kuang, R.: Fast string kernels using inexact matching for protein sequences. Journal of Machine Learning Research 5 (2004) 1435-1455
13. Rousu, J., Shawe-Taylor, J.: Efficient computation of gapped substring kernels for large alphabets. Journal of Machine Leaning Research 6 (2005) 1323-1344
14. Schölkopf, B.: The kernel trick for distances. In Leen, T., Diettrich, T., Tresp, V., eds.: Advances in Neural Information Processing Systems 13, MIT Press (2001)
15. Jaccard, P.: Contribution au problème de l'immigration post-glaciaire de la flore alpine. Bulletin de la Société Vaudoise des Sciences Naturelles 36 (1900) 87-130
16. Anderberg, M.: Cluster Analysis for Applications. Academic Press, Inc., New York, NY, USA (1973)
17. Knuth, D.: The art of computer programming. Volume 3. Addison-Wesley (1973)
18. Shawe-Taylor, J., Cristianini, N.: Kernel methods for pattern analysis. Cambridge University Press (2004)
19. Sonnenburg, S., Zien, A., Ratsch, G.: ARTS: Accurate recognition of transcription starts in human. Bioinformatics (2006) submitted.
20. Lippmann, R., Haines, J., Fried, D., Korba, J., Das, K.: The 1999 DARPA off-line intrusion detection evaluation. Computer Networks 34(4) (2000) 579-595
21. Lewis, D.D.: Reuters-21578 text categorization test collection. AT&T Labs Research (1997)
22. Rieck, K., Laskov, P.: Detecting unknown network attacks using language models. In: Proc. DIMVA. (2006) to appear.