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

Result code in 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>
Alternative codes found
RIV/68407700:21240/13:00203181
Result on the web
<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>

Result language
angličtina
Original language name
Supervised two-step feature extraction for structured representation of text data
Original language description
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.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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