Training set fuzzification towards prediction improvement

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F17%3AA1801QXX" target="_blank" >RIV/61988987:17310/17:A1801QXX - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-59650-1_18" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-59650-1_18</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-59650-1_18" target="_blank" >10.1007/978-3-319-59650-1_18</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Training set fuzzification towards prediction improvement

Popis výsledku v původním jazyce

This article presents a method of fuzzification of variables using a histogram. This approach is used when creating an output vector of a training set that forms linguistic variables. An appropriate transformation of an input vector of the training sets was also proposed. Both of the aforesaid procedures were described in detail in the article. An extensive comparative experimental study with the following outcomes was carried out. The neural net which was adapted by the transformed training set showed a significantly better prediction than a neural network which was adapted by a training set without making any changes. The results of this experimental study were analyzed in the conclusion.

Název v anglickém jazyce

Training set fuzzification towards prediction improvement

Popis výsledku anglicky

This article presents a method of fuzzification of variables using a histogram. This approach is used when creating an output vector of a training set that forms linguistic variables. An appropriate transformation of an input vector of the training sets was also proposed. Both of the aforesaid procedures were described in detail in the article. An extensive comparative experimental study with the following outcomes was carried out. The neural net which was adapted by the transformed training set showed a significantly better prediction than a neural network which was adapted by a training set without making any changes. The results of this experimental study were analyzed in the conclusion.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Artificial Intelligence

ISBN

978-331959649-5

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

13

Strana od-do

207-219

Název nakladatele

Springer Verlag

Místo vydání

Cham, Switzerland

Místo konání akce

La Rioja; Spain

Datum konání akce

21. 6. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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