Correlations of Random Classifiers on Large Data Sets

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Correlations of Random Classifiers on Large Data Sets

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Correlations of Random Classifiers on Large Data Sets

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA19-05704S" target="_blank" >GA19-05704S: FoNeCo: Analytické základy neurovýpočtů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Soft Computing

ISSN

1432-7643

e-ISSN

1433-7479

Svazek periodika

25

Číslo periodika v rámci svazku

19

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

8

Strana od-do

12641-12648

Kód UT WoS článku

000661788300004

EID výsledku v databázi Scopus

2-s2.0-85107955110