Enhanced ensemble-based classifier with boosting for pattern recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F17%3AA1801NAQ" target="_blank" >RIV/61988987:17310/17:A1801NAQ - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S0096300317302710" target="_blank" >http://www.sciencedirect.com/science/article/pii/S0096300317302710</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.amc.2017.04.019" target="_blank" >10.1016/j.amc.2017.04.019</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhanced ensemble-based classifier with boosting for pattern recognition

Popis výsledku v původním jazyce

The aim of the article is a proposal of a classifier based on neural networks that will be applicable in machine digitization of incomplete and inaccurate data or data containing noise for the purpose of their classification (pattern recognition). The article is focused on the possibility of increasing the efficiency of the algorithms via their appropriate combination, and particularly increasing their reliability and reducing their time demands. Time demands do not mean runtime, nor its development, but time demands of applying the algorithm to a particular problem domain. In other words, the amount of professional labour that is needed for such an implementation. The article aims at methods from the field of pattern recognition, which primarily means various types of neural networks. The proposed approaches are verified experimentally.

Název v anglickém jazyce

Enhanced ensemble-based classifier with boosting for pattern recognition

Popis výsledku anglicky

The aim of the article is a proposal of a classifier based on neural networks that will be applicable in machine digitization of incomplete and inaccurate data or data containing noise for the purpose of their classification (pattern recognition). The article is focused on the possibility of increasing the efficiency of the algorithms via their appropriate combination, and particularly increasing their reliability and reducing their time demands. Time demands do not mean runtime, nor its development, but time demands of applying the algorithm to a particular problem domain. In other words, the amount of professional labour that is needed for such an implementation. The article aims at methods from the field of pattern recognition, which primarily means various types of neural networks. The proposed approaches are verified experimentally.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

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 periodika

APPL MATH COMPUT

ISSN

0096-3003

e-ISSN

—

Svazek periodika

310

Číslo periodika v rámci svazku

OCT 1 2017

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

1-14

Kód UT WoS článku

000402488400001

EID výsledku v databázi Scopus

2-s2.0-85018275584