Classification of large data sets by neural networks: A probabilistic viewpoint
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00638481" target="_blank" >RIV/67985807:_____/25:00638481 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-032-04558-4_38" target="_blank" >http://dx.doi.org/10.1007/978-3-032-04558-4_38</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-032-04558-4_38" target="_blank" >10.1007/978-3-032-04558-4_38</a>
Alternative languages
Result language
angličtina
Original language name
Classification of large data sets by neural networks: A probabilistic viewpoint
Original language description
A probabilistic approach to the classification of large data sets is presented. For data drawn from distributions that do not satisfy the naive Bayes assumption (when the presence of features is not independent of one another), conditions on the distributions are given that guarantee the almost deterministic behavior of errors in approximation by neural networks. It is shown that mean values of correlations with network computational units, together with the growth of sizes of their sets of input/output functions, can be used to assess the suitability of networks for classes of tasks characterized by probabilities modeling their relevance for a given type of applications.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA25-15490S" target="_blank" >GA25-15490S: LEDNeCo: Low Energy Deep Neurocomputing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
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 and Machine Learning – ICANN 2025. Proceedings, Part I
ISBN
978-3-032-04557-7
ISSN
0302-9743
e-ISSN
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Number of pages
7
Pages from-to
480-486
Publisher name
Springer
Place of publication
Cham
Event location
Kaunas
Event date
Sep 9, 2025
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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