Signature verification using unsupervised learned neural networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F03%3A00000029" target="_blank" >RIV/49777513:23520/03:00000029 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Signature verification using unsupervised learned neural networks
Original language description
The Carpenter-Grosberg`s ART-2 and Kohonen`s Selforganizing Feature Map (SOFM) have been developed for the clustering of input vectors and have been commonly used as unsupervised learned classifiers. In this paper we describe the use of these neural network models for signature verification. The architecture of the verifiers and achieved results are discussed here and ideas for future research are also suggested.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2003
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks in Pattern Recognition
ISBN
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ISSN
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e-ISSN
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Number of pages
5
Pages from-to
71-75
Publisher name
University of Florence
Place of publication
Florence
Event location
Florencie
Event date
Sep 11, 2003
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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