Abstract
Several algorithms including BM, BMG, AC and AC-BM are discussed and the running time of the algorithms are measured on the snort in the paper. The results show that AC and AC-BM are faster than BM and BMG on the large number of patterns, but on the small number of patterns we obtained the opposite result.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. of CVPR, Kauai Marriott, Hawaii, pp. 511–518 (2001)
Freund, Y., Sharpie, R.E.: A decision-theoretic generalization of online learning and an application to Boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Friedman, J.H., Trevor, R.T.: Additive logistic regression: A statistical view of boosting. The Annals of Statistics 38(2), 337–374 (2000)
Aho, A.V., Corasick, M.J.: Efficient String Matching: An Aid to Bibliographic Search. Communications of the ACM 18(6), 333–343 (1975)
Commentz-Walter, B.: A string matching algorithm fast on the average. In: Proc. 6th International Colloquium on Automata, Languages, and Programming, pp. 118–132 (1979)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, M., Zhu, L. (2012). Research about Pattern Matching Algorithm. In: Zhang, T. (eds) Instrumentation, Measurement, Circuits and Systems. Advances in Intelligent and Soft Computing, vol 127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27334-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-27334-6_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27333-9
Online ISBN: 978-3-642-27334-6
eBook Packages: EngineeringEngineering (R0)