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JACIII Vol.11 No.6 pp. 662-667
doi: 10.20965/jaciii.2007.p0662
(2007)

Paper:

Fuzzy Observable Markov Models for Pattern Recognition

Dat Tran, Wanli Ma, and Dharmendra Sharma

School of Information Sciences and Engineering, University of Canberra, ACT 2601, Australia

Received:
January 15, 2007
Accepted:
March 20, 2007
Published:
July 20, 2007
Keywords:
observable Markov model, written language recognition, spam email recognition, typist recognition, fuzzy modeling
Abstract

This paper presents a mathematical framework for fuzzy discrete and continuous observable Markov models (OMMs) and their applications to written language, spam email and typist recognition. Experimental results show that the proposed OMMs are more effective than models such as vector quantization, Gaussian mixture model and hidden Markov model.

Cite this article as:
D. Tran, W. Ma, and D. Sharma, “Fuzzy Observable Markov Models for Pattern Recognition,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.6, pp. 662-667, 2007.
Data files:
References
  1. [1] H. B. Aradhye, G. K. Myers, and J. A. Herson, “Image Analysis for Efficient Categorization of Image-based Spam E-mail,” in Proc. of ICDAR, 2005.
  2. [2] W. B. Cavnar and J. M. Trenkle, “N-gram-based text categorization,” in Proc. of the 3rd Annual Symp. Document Analysis and Information, Retrieval, 1994.
  3. [3] Z. Chuan, L. Xianliang, and X. Qian, “A Novel Anti-spam Email Approach Based on LVQ,” in Lecture Notes in Computer Science, Vol.3320, pp. 180-183, 2003.
  4. [4] R. O. Duda and P. E. Hart, “Pattern classification and scene analysis,” John Wiley & Sons, New York, 1973.
  5. [5] F. D. Garcia, J.-H. Hoepman, and J. van Nieuwenhuizen, “Spam Filter Analysis,” Lecture Notes in Computer Science, Vol.147, pp. 395-410, 2004.
  6. [6] X. D. Huang, Y. Ariki, and M. A. Jack, “Hidden Markov Models For Speech Recognition,” Edinburgh University Press, 1990.
  7. [7] N. Kang, C. Domeniconi, and D. Barbara, “Categorization and Keyword Identification of Unlabeled Documents,” Proc. of the 5th IEEE International Conference on Data Mining, pp. 677-680, 2005.
  8. [8] W. Ma, D. Tran, and D. Sharma, “Detecting Spam Email by Extracting Keywords from Image Attachments,” Proc. of Int. Conf. on Hybrid Information Technology, Korea, 2006.
  9. [9] W. Ma, D. Tran, and D. Sharma, “Detecting image based spam email by using OCR and trigram methods,” Proc. of Asia-Pacific Workshop on Visual Information Processing, China, 2006.
  10. [10] L. R. Rabiner and B. H. Juang, “Fundamentals of speech recognition,” Prentice Hall PTR, USA, 1993.
  11. [11] L. R. Rabiner and B. H. Juang, “An introduction to hidden Markov models,” IEEE Acoustic, Speech, and Signal Processing Society Magazine, Vol.3, No.1, pp. 4-16, 1986.
  12. [12] M. Sasaki and H. Shinnou, “Spam Detection Using Text Clustering,” Proc. of the International Conference on Cyberworlds, pp. 316-319, 2005.
  13. [13] D. Tran, “Fuzzy Observable Markov Model for Cell Phase Identification,” Proc. SCIS-ISIS, pp. 1122-1125, 2006.
  14. [14] D. Tran and T. Pham, “Cell Phase Classification Using Markov and Gaussian Mixture Models,” Proc. of Asia-Pacific Workshop on Visual Information Processing, pp. 48-52, 2005.
  15. [15] D. Tran and D. Sharma, “Markov Models for Written Language Identification,” Proc. ICONIP, pp. 67-70, 2005.
  16. [16] D. Tran and M. Wagner, “Generalised Fuzzy Hidden Markov Models for Speech Recognition,” Lecture Notes in Computer Science, pp. 345-351, Springer-Verlag, 2002.
  17. [17] D. Tran and M. Wagner, “Fuzzy hidden Markov models for speech and speaker recognition,” Proc. NAFIPS, pp. 426-430, USA, 1999.

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