All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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