Strictly modular probabilistic neural networks for pattern recognition.

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F03%3A16030252" target="_blank" >RIV/67985556:_____/03:16030252 - isvavai.cz</a>

Alternative codes found

RIV/61384399:31160/03:00018232

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Strictly modular probabilistic neural networks for pattern recognition.

Original language description

Considering the statistical pattern recognition we approximate the unknown class-conditional probability distributions by multivariate Bernoulli mixtures. We show that both the parameter optimization based on EM algorithm and the resulting Bayesian decision-making can be realized by a strictly modular probabilistic neural network. The autonomous adaptation of neurons includes only the locally available information. The properties of the sequential learning procedure are illustrated by numerical examples.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2003

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Network World

ISSN

1210-0552

e-ISSN

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Volume of the periodical

13

Issue of the periodical within the volume

6

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

17

Pages from-to

599-615

UT code for WoS article

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EID of the result in the Scopus database

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