Multiple classifier fusion in probabilistic neural networks

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SYSNO ASEP	0410828
Document Type	J - Journal Article
R&D Document Type	Journal Article
Subsidiary J	Ostatní články
Title	Multiple classifier fusion in probabilistic neural networks
Author(s)	Grim, Jiří (UTIA-B)_{RID, ORCID} Kittler, J. (GB) Pudil, Pavel (UTIA-B)_RID Somol, Petr (UTIA-B)_RID
Source Title	Pattern Analysis and Applications - ISSN 1433-7541 Roč. 5, č. 7 (2002), s. 221-233
Number of pages	13 s.
Language	eng - English
Country	GB - United Kingdom
Keywords	EM algorithm ; information preserving transform ; multiple classifier fusion
Subject RIV	BB - Applied Statistics, Operational Research
R&D Projects	GA402/01/0981 GA ČR - Czech Science Foundation (CSF)
CEZ	AV0Z1075907 - UTIA-B
Annotation	The main motivation of the present paper is to design a statistically well justified and biologically compatible neural network model and to suggest a theoretical interpretation of the high parallelism of biological neural networks. We consider a probabilistic approach to neural networks in the framework of statistical pattern recognition. The complete method based on EM algorithm has been applied to recognize unconstrained handwritten numerals from the database of the Concordia University Montreal.
Workplace	Institute of Information Theory and Automation
Contact	Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.

Number of the records: 1