On Robust Information Extraction from High-Dimensional Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00427963" target="_blank" >RIV/67985807:_____/14:00427963 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5937/sjm9-5520" target="_blank" >http://dx.doi.org/10.5937/sjm9-5520</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5937/sjm9-5520" target="_blank" >10.5937/sjm9-5520</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Robust Information Extraction from High-Dimensional Data

Popis výsledku v původním jazyce

Information extraction from high-dimensional data represents an important problem in current applications in management or econometrics. An important problem from a practical point of view is the sensitivity of machine learning methods with respect to the presence of outlying data values, while numerical stability represents another important aspect of data mining from high-dimensional data. This paper gives an overview of various types of data mining, discusses their suitability for high-dimensional data and critically discusses their properties from the robustness point of view, while we explain that the robustness itself is perceived differently in different contexts. Moreover, we investigate properties of a robust nonlinear regression estimator ofKalina (2013).

Název v anglickém jazyce

On Robust Information Extraction from High-Dimensional Data

Popis výsledku anglicky

Information extraction from high-dimensional data represents an important problem in current applications in management or econometrics. An important problem from a practical point of view is the sensitivity of machine learning methods with respect to the presence of outlying data values, while numerical stability represents another important aspect of data mining from high-dimensional data. This paper gives an overview of various types of data mining, discusses their suitability for high-dimensional data and critically discusses their properties from the robustness point of view, while we explain that the robustness itself is perceived differently in different contexts. Moreover, we investigate properties of a robust nonlinear regression estimator ofKalina (2013).

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Serbian Journal of Management

ISSN

1452-4864

e-ISSN

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Svazek periodika

9

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

RS - Srbská republika

Počet stran výsledku

14

Strana od-do

131-144

Kód UT WoS článku

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EID výsledku v databázi Scopus

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