Multi-prototype classification: Improved modelling of the variability of handwritten data using statistical clustering algorithms

Rahman, Ahmad Fuad Rezaur, Fairhurst, Michael (1997) Multi-prototype classification: Improved modelling of the variability of handwritten data using statistical clustering algorithms. Electronics Letters, 33 (14). pp. 1208-1210. ISSN 0013-5194. (doi:10.1049/el:19970848) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:17892)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL: http://dx.doi.org/10.1049/el:19970848

Abstract

The principal obstacle in successfully recognising handwritten data is thr inherent degree of intra-class variability encountered, This calls for subclass modelling of handwritten data based on the statistically significant variations within the main classes. A novel multi-prototyping approach based on statistical clustering techniques is investigated as an appropriate solution to this problem and very encouraging results have been achieved.

Item Type:	Article
DOI/Identification number:	10.1049/el:19970848
Subjects:	T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User:	T.J. Sango
Date Deposited:	21 May 2009 09:01 UTC
Last Modified:	05 Nov 2024 09:53 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/17892 (The current URI for this page, for reference purposes)

University of Kent Author Information

Fairhurst, Michael.

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