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<dc:title xml:lang="pl"><![CDATA[Efficient decision trees for multi-class support vector machines using entropy and generalization error estimation]]></dc:title>
<dc:creator><![CDATA[Kantavat, Pittipol]]></dc:creator>
<dc:creator><![CDATA[Kijsirikul, Boonserm]]></dc:creator>
<dc:creator><![CDATA[Songsiri, Patoomsiri]]></dc:creator>
<dc:creator><![CDATA[Fukui, Ken-Ichi]]></dc:creator>
<dc:creator><![CDATA[Numao, Masayuki]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[support vector machines]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[multi-class classification]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[generalization error]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[entropy]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[decision trees]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[We propose new methods for support vector machines using a tree architecture for multi-class classification. In each node of the tree, we select an appropriate binary classifier, using entropy and generalization error estimation, then group the examples into positive and negative classes based on the selected classifier, and train a new classifier for use in the classification phase. The proposed methods can work in time complexity between O(log2 N) and O(N), where N is the number of classes.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[We compare the performance of our methods with traditional techniques on the UCI machine learning repository using 10-fold cross-validation. The experimental results show that the methods are very useful for problems that need fast classification time or those with a large number of classes, since the proposed methods run much faster than the traditional techniques but still provide comparable accuracy.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Korbicz, Józef (1951- ) - red.]]></dc:contributor>
<dc:contributor><![CDATA[Uciński, Dariusz - red.]]></dc:contributor>
<dc:date><![CDATA[2018]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/85901/AMCS_2018_28_4_8.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/100893/edition/85901/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:85901]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 28, number 4 (2018)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=110]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:100893]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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