Supervised two-step feature extraction for structured representation of text data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00203181" target="_blank" >RIV/68407700:21230/13:00203181 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/13:00203181

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S1569190X12001578" target="_blank" >http://www.sciencedirect.com/science/article/pii/S1569190X12001578</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.simpat.2012.11.003" target="_blank" >10.1016/j.simpat.2012.11.003</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Supervised two-step feature extraction for structured representation of text data

Popis výsledku v původním jazyce

Training data matrix used for classification of text documents to multiple categories is characterized by large number of dimensions while the number of manually classified training documents is relatively small. Thus the suitable dimensionality reduction techniques are required to be able to develop the classifier. The article describes two-step supervised feature extraction method that takes advantage of projections of terms into document and category spaces. We propose several enhancements that makethe method more efficient and faster than it was presented in our former paper. We also introduce the adjustment score that enables to correct defected targets or helps to identify improper training examples that bias extracted features.

Název v anglickém jazyce

Supervised two-step feature extraction for structured representation of text data

Popis výsledku anglicky

Training data matrix used for classification of text documents to multiple categories is characterized by large number of dimensions while the number of manually classified training documents is relatively small. Thus the suitable dimensionality reduction techniques are required to be able to develop the classifier. The article describes two-step supervised feature extraction method that takes advantage of projections of terms into document and category spaces. We propose several enhancements that makethe method more efficient and faster than it was presented in our former paper. We also introduce the adjustment score that enables to correct defected targets or helps to identify improper training examples that bias extracted features.

Druh

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

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Simulation Modelling Practice and Theory

ISSN

1569-190X

e-ISSN

—

Svazek periodika

33

Číslo periodika v rámci svazku

33

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

132-143

Kód UT WoS článku

000317253700011

EID výsledku v databázi Scopus

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