Document Classification with Supervised Latent Feature Selection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00197792" target="_blank" >RIV/68407700:21230/12:00197792 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/12:00197792
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Document Classification with Supervised Latent Feature Selection
Original language description
The classification of text documents generally deals with large dimensional data. To favor generality of classification researcher has to apply a dimensionality reduction technique before building a classifier. We propose classification and reduction algorithm that makes use of latent uncorrelated topics extracted from training documents and their known categories. We suggest several latent feature selection options and provide their testing.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
ISBN
978-1-4503-0915-8
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
70-74
Publisher name
ACM
Place of publication
New York
Event location
Craiova
Event date
Jul 13, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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