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@inproceedings{568397,
author = {Hroza, Jiří and Žižka, Jan},
address = {Germany},
booktitle = {Computational linguistics and Intelligent Text Processing},
keywords = {machine learning; text categorization; text filtration; text similarity; k-NN; ranking},
language = {eng},
location = {Germany},
isbn = {3-540-24523-5},
pages = {608-611},
publisher = {Springer-Verlag Berlin Heidelberg},
title = {Selecting Interesting Articles Using Their Similarity Based Only on Positive Examples},
year = {2005}
}
TY - JOUR
ID - 568397
AU - Hroza, Jiří - Žižka, Jan
PY - 2005
TI - Selecting Interesting Articles Using Their Similarity Based Only on Positive Examples
PB - Springer-Verlag Berlin Heidelberg
CY - Germany
SN - 3540245235
KW - machine learning
KW - text categorization
KW - text filtration
KW - text similarity
KW - k-NN
KW - ranking
N2 - The task of automated searching for interesting text documents frequently suffers from a~very poor balance among documents representing both positive and negative examples or from one completely missing class. This paper suggests the ranking approach based on the k-NN algorithm adapted for determining the similarity degree of new documents just to the representative positive collection. From the viewpoint of the precision-recall relation, a~user can decide in advance how many and how similar articles should be released through a filter.
ER -
HROZA, Jiří a Jan ŽIŽKA. Selecting Interesting Articles Using Their Similarity Based Only on Positive Examples. Online. In \textit{Computational linguistics and Intelligent Text Processing}. Germany: Springer-Verlag Berlin Heidelberg, 2005. s.~608-611. ISBN~3-540-24523-5. [citováno 2024-04-24]
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