<?xml version="1.0" encoding="UTF-8"?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/
                             http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
  <responseDate>2026-09-24T19:48:26Z</responseDate>
  <request verb="GetRecord" identifier="oai:vu.lt:elaba:1999002" metadataPrefix="oai_dc">https://epublications.vu.lt/oai</request>
<GetRecord>
<record>
  <header>
    <identifier>oai:vu.lt:elaba:1999002</identifier>
    <datestamp>2026-07-06T23:27:20Z</datestamp>
    <setSpec>openaire</setSpec><setSpec>NDLTD</setSpec><setSpec>DRIVER</setSpec>
  </header>
  <metadata>
<oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/"><dc:title>Neuroninių tinklų architektūros parinkimas /</dc:title><dc:title>Selection of the neural network architecture.</dc:title><dc:creator>Verbel, Irina,</dc:creator><dc:rights>info:eu-repo/semantics/openAccess</dc:rights><dc:description>In this thesis a novel technique is used to construct sparse generalized Gaussian Kernel regression model- so called neural network. Kernel which maximize Renyi entropy is used too. Experimental results obtained using these models are promising.</dc:description><dc:publisher>Institutional Repository of Vilnius University</dc:publisher><dc:contributor>Vaitkus, Pranas</dc:contributor><dc:type>info:eu-repo/semantics/masterThesis</dc:type><dc:language>lit</dc:language><dc:date>2009</dc:date><dc:format>application/pdf</dc:format><dc:relation>https://epublications.vu.lt/object/elaba:1999002/1999002.pdf</dc:relation><dc:identifier>https://repository.vu.lt/VU:ELABAETD1999002&amp;prefLang=en_US</dc:identifier></oai_dc:dc>  </metadata>
</record>

</GetRecord></OAI-PMH>
