Correlations of Random Classifiers on Large Data Sets
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00543168" target="_blank" >RIV/67985807:_____/21:00543168 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/s00500-021-05938-4" target="_blank" >http://dx.doi.org/10.1007/s00500-021-05938-4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00500-021-05938-4" target="_blank" >10.1007/s00500-021-05938-4</a>
Alternative languages
Result language
angličtina
Original language name
Correlations of Random Classifiers on Large Data Sets
Original language description
Classification of large data sets by feedforward neural networks is investigated. To deal with unmanageably large sets of classification tasks, a probabilistic model of their relevance is considered. Optimization of networks computing randomly chosen classifiers is studied in terms of correlations of classifiers with network input-output functions. Effects of increasing sizes of sets of data to be classified are analyzed using geometrical properties of high-dimensional spaces. Their consequences on concentrations of values of sufficiently smooth functions of random variables around their mean values are applied. It is shown that the critical factor for suitability of a class of networks for computing randomly chosen classifiers is the maximum of sizes of the mean values of their correlations with network input-output functions. To include cases in which function values are not independent, the method of bounded differences is exploited.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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/GA19-05704S" target="_blank" >GA19-05704S: FoNeCo: Analytical Foundations of Neurocomputing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Soft Computing
ISSN
1432-7643
e-ISSN
1433-7479
Volume of the periodical
25
Issue of the periodical within the volume
19
Country of publishing house
DE - GERMANY
Number of pages
8
Pages from-to
12641-12648
UT code for WoS article
000661788300004
EID of the result in the Scopus database
2-s2.0-85107955110