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<dc:title xml:lang="pl"><![CDATA[A fast neural network learning algorithm with approximate singular value decomposition]]></dc:title>
<dc:creator><![CDATA[Jankowski, Norbert]]></dc:creator>
<dc:creator><![CDATA[Linowiecki, Rafał]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[Moore?Penrose pseudo-inverse learning]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[radial basis function network]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[extreme learning machines]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[kernel methods]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[machine learning]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[Singular Value Decomposition (SVD)]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[deep extreme learning]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[principal component analysis (PCA)]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[The learning of neural networks is becoming more and more important. Researchers have constructed dozens of learning algorithms, but it is still necessary to develop faster, more flexible, or more accurate learning algorithms. With fast learning we can examine more learning scenarios for a given problem, especially in the case of meta-learning. In this article we focus on the construction of a much faster learning algorithm and its modifications, especially for nonlinear versions of neural networks.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[The main idea of this algorithm lies in the usage of fast approximation of the Moore - Penrose pseudo-inverse matrix. The complexity of the original singular value decomposition algorithm is O(mn2). We consider algorithms with a complexity of O(mnl), where l < n and l is often significantly smaller than n. Such learning algorithms can be applied to the learning of radial basis function networks, extreme learning machines or deep ELMs, principal component analysis or even missing data imputation.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Kulczycki, Piotr - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Kacprzyk, Janusz - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Kóczy, László T. - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Mesiar, Radko - ed.]]></dc:contributor>
<dc:date><![CDATA[2019]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/86008/AMCS_2019_29_3_13.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/100993/edition/86008/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:86008]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 29, number 3 (2019)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=113]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:100993]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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