Extracting features for the linguistic variables of fuzzy rules using hidden Markov model

Citation

Suliman, Azizah and Sulaiman, Md. Nasir and Othman, Mohamed and O. K. Rahmat, Rahmita Wirza (2007) Extracting features for the linguistic variables of fuzzy rules using hidden Markov model. In: International Electronic Conference on Computer Science 2007 (IeCCS 2007), 28 June-8 July 2007 & 30 Nov.-10 Dec. 2007 (pp. 30-33).

Abstract

In classifying handwritten characters, the stages prior to the classification phase play a role as major as the classification itself. This research work will be classifying the characters using a syntactical classification method namely fuzzy logic but will use the statistical method of Hidden Markov Model as an approach in extracting features for the linguistic variables of the fuzzy rule‐based system. In this paper the feature extraction method will be highlighted and detailed. The HMM Model of a variable to be used in the classification system will be discussed. Experimental results from a few sample images show that the proposed technique is both effective and efficient to be used in extracting features for the linguistic variables of fuzzy rules.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Computer Science and Information Technology
DOI Number:	https://doi.org/10.1063/1.3037080
Publisher:	American Institute of Physics
Keywords:	HMM model; Linguistic variable; Fuzzy logic; Handwritten character recognition
Depositing User:	Nabilah Mustapa
Date Deposited:	26 Sep 2017 04:02
Last Modified:	26 Sep 2017 04:02
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1063/1.3037080
URI:	http://psasir.upm.edu.my/id/eprint/57313
Statistic Details:	View Download Statistic

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