Abstract
One of the main problems in the syntactic pattern recognition area concerns analysis of distorted/fuzzy string patterns. Classical methods developed to solve the problem are based on the error-correcting approach or the stochastic one. These methods are useful but have several limitations. Therefore, there is still the need to construct effective models of syntactic recognition of distorted/fuzzy patterns. The new approach to the problem is presented in the paper. It is based on the fuzzy primitives and the new class of fuzzy automata. The advantages of the approach are presented in the paper, as well as its comparison to classical approaches.
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References
Aho, A.V., Peterson, T.G.: A Minimum Distance Error-correcting Parser for Context-free Languages. SIAM J. Comput. 4, 305–317 (1972)
Aho, A.V., Ullman, J.D.: The Theory of Parsing, Translation, and Compiling. Prentice-Hall, Englewood Cliffs (1972)
Bunke, H.O., Sanfeliu, A. (eds.): Syntactic and Structural Pattern Recognition – Theory and Applications. World Scientific, Singapore (1990)
Doostfatemeh, M., Kremer, S.C.: New directions in fuzzy automata. International Journal of Approximate Reasoning 38, 175–214 (2005)
Flasiński, M., Jurek, J.: Dynamically Programmed Automata for Quasi Context Sensitive Languages as a Tool for Inference Support in Pattern Recognition-Based Real-Time Control Expert Systems. Pattern Recognition 32, 671–690 (1999)
Flasiński, M., Reroń, E., Jurek, J., Wójtowicz, P., Atıasiewicz, K.: On the construction of the syntactic pattern recognition-based expert system for auditory brainstem response analysis. In: Kurzyński, M., Puchaıa, E., Woźniak, M., Żoınierek, A. (eds.) Computer Recognition Systems. ASC, vol. 30, pp. 503–510. Springer, Heidelberg (2005)
Flasiński, M., Jurek, J.: On the Analysis of Fuzzy String Patterns with the Help of Extended and Stochastic GDPLL(k) Grammars. Fundamenta Informaticae 71, 1–14 (2006)
Flasiński, M., Jurek, J.: Fundamental Methodological Issues of Syntactic Pattern Recognition. Pattern Analysis and Applications 17, 465–480 (2014)
Flasiński, M., Jurek, J., Peszek, T.: Parallel processing model for syntactic pattern recognition-based electrical load forecast. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2013, Part I. LNCS, vol. 8384, pp. 338–347. Springer, Heidelberg (2014)
Flasiński, M., Jurek, J., Peszek, T.: Application of Syntactic Pattern Recognition Methods for Electrical Load Forecasting. Advances in Intelligent Systems and Computing. Springer (in print)
Freeman, H.: On the Encoding of Arbitrary Geometric Configurations. IEEE Trans. Electron. Comput. EC–10, 260–268 (1961)
Fu, K.S.: Syntactic Pattern Recognition and Applications. Prentice Hall (1982)
Gonzales, R.C., Thomason, M.G.: Syntactic Pattern Recognition: An Introduction. Addison-Wesley, Reading (1978)
Jurek, J.: Towards grammatical inferencing of GDPLL(k) grammars for applications in syntactic pattern recognition-based expert systems. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 604–609. Springer, Heidelberg (2004)
Jurek, J.: Recent Developments of the Syntactic Pattern Recognition Model Based on Quasi-context Sensitive Languages. Pattern Recognition Letters 26, 1011–1018 (2005)
Jurek, J.: Grammatical inference as a tool for constructing self-learning syntactic pattern recognition-based agents. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part III. LNCS, vol. 5103, pp. 712–721. Springer, Heidelberg (2008)
Jurek, J., Peszek, T.: Model of syntactic recognition of distorted string patterns with the help of GDPLL(k)-based automata. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds.) CORES 2013. AISC, vol. 226, pp. 101–110. Springer, Heidelberg (2013)
Ogiela, M.R., Ogiela, L., Tadeusiewicz, R.: Mathematical Linguistic in Cognitive Medical Images Interpretation Systems. Journal of Mathematical Imaging and Vision 34, 328–340 (2009)
Pavlidis, T.: Structural Pattern Recognition. Springer, New York (1977)
Rosenkrantz, D.J.: Programmed Grammars and Classes of Formal Languages. J. ACM 16, 107–131 (1969)
Shaw, A.C.: A Formal Picture Description Scheme as Basis for Picture Processing Systems. Information and Control 14, 9–52 (1969)
Specht, D.F.: Probabilistic Neural Networks. Neural Networks 3, 109–118 (1990)
Tadeusiewicz, R., Ogiela, M.R.: Medical Image Understanding Technology. Springer, Heidelberg (2004)
Tanaka, E.: Theoretical Aspects of Syntactic Pattern Recognition. Pattern Recognition 28, 1053–1061 (1995)
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Flasiński, M., Jurek, J., Peszek, T. (2016). Analysis of Fuzzy String Patterns with the Help of Syntactic Pattern Recognition. In: Andreasen, T., et al. Flexible Query Answering Systems 2015. Advances in Intelligent Systems and Computing, vol 400. Springer, Cham. https://doi.org/10.1007/978-3-319-26154-6_9
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DOI: https://doi.org/10.1007/978-3-319-26154-6_9
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