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Generation of Pattern Classifiers Based on Linear Nongroup CA
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 Title & Authors
Generation of Pattern Classifiers Based on Linear Nongroup CA
Choi, Un-Sook; Cho, Sung-Jin; Kim, Han-Doo;
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Nongroup Cellular Automata(CA) having two trees in the state transition diagram of a CA is suitable for pattern classifier which divides pattern set into two classes. Maji et al. [1] classified patterns by using multiple attractor cellular automata as a pattern classifier with dependency vector. In this paper we propose a method of generation of a pattern classifier using feature vector which is the extension of dependency vector. In addition, we propose methods for finding nonreachable states in the 0-tree of the state transition diagram of TPMACA corresponding to the given feature vector for the analysis of the state transition behavior of the generated pattern classifier.
Pattern Classifier;Cellular Automata(CA);Multiple Attractor CA(MACA);Nonreachable state;Feature Vector;
 Cited by
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S.J. Cho, U.S. Choi, H.D. Kim, Y.H. Hwang, J.G. Kim, and S.H. Heo, "New Synthesis of One-dimensional 90/150 Linear Hybrid Group Cellular Automata," IEEE Transaction on Computer-Aided Design of Integrated Circuits Systems, Vol. 26, No. 9, pp. 1720-1724, 2007. crossref(new window)

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S.J. Cho, H.D. Kim, U.S. Choi, S.T. Kim, J.G. Kim, S.H. Kwon, et al., "Generation of TPMACA for Pattern Classification," Lecture Notes in Computer Science, Vol. 8751, pp. 408-416, 2014.

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