Computational Properties of Probabilistic Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F10%3A00350163" target="_blank" >RIV/67985556:_____/10:00350163 - isvavai.cz</a>
Alternative codes found
RIV/61384399:31160/10:00036181
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Computational Properties of Probabilistic Neural Networks
Original language description
We discuss the problem of overfitting of probabilistic neural networks in the framework of statistical pattern recognition. The probabilistic approach to neural networks provides a statistically justified subspace method of classification. The underlyingstructural mixture model includes binary structural parameters and can be optimized by EM algorithm in full generality. Formally, the structural model reduces the number of parameters included and therefore the structural mixtures become less complex and less prone to overfitting. We illustrate how recognition accuracy and the effect of overfitting is influenced by mixture complexity and by the size of training data set.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2010
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 ? ICANN 2010
ISBN
978-3-642-15818-6
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
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Publisher name
Springer Verlag
Place of publication
Berlin Heidelberg
Event location
Thessaloniki
Event date
Sep 15, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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