Journal article Open Access

Using Derived kernel as a new Method for Recognition a Similarity Learning.

Ramadhan A. M. Alsaidi,; Ayed R.A. Alanzi; Saleh R. A. Alenazi; Madallah Alruwaili


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  <identifier identifierType="URL">https://zenodo.org/record/5577067</identifier>
  <creators>
    <creator>
      <creatorName>Ramadhan A. M. Alsaidi,</creatorName>
      <familyName>Ramadhan A. M. Alsaidi</familyName>
      <affiliation>department of Mathematics, Jouf  University, Gurayat, Saudi Arabia.</affiliation>
    </creator>
    <creator>
      <creatorName>Ayed R.A. Alanzi</creatorName>
      <affiliation>department of Mathematics, Majmaah University,  Majmaah 11952, Saudi Arabia.</affiliation>
    </creator>
    <creator>
      <creatorName>Saleh R. A. Alenazi</creatorName>
      <affiliation>Computer Technology department, Tabuk College  of Technical, Tabuk, Saudi Arabia</affiliation>
    </creator>
    <creator>
      <creatorName>Madallah Alruwaili</creatorName>
      <affiliation>College of Computer snd Information Sciences,  Jouf University, Skaka, Aljouf, Saudi Arabia.</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Using Derived kernel as a new Method for Recognition a Similarity Learning.</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Feature Extraction; Hierarchical Learning; Entropy Measures; Pearson Correlation Coefficient; Pooling Operation; Sample.</subject>
    <subject subjectScheme="issn">2249-8958</subject>
    <subject subjectScheme="handle">C5705029320/2020©BEIESP</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  &amp; Sciences Publication (BEIESP)</contributorName>
      <affiliation>Publisher</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2020-02-29</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5577067</alternateIdentifier>
  </alternateIdentifiers>
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    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.C5705.029320</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;A new technique for feature withdrawal by neural response is going to be familiarized in this research work by merging an entropy measure with Squared Pearson correlation Coefficient (SPCC) method. The process of choosing effective models on the basis of entropy measures was proposed further to enhance the ability to select templates. For more accurate similarity measure we used the statistical significant relationship between functions. The research illustrate that the proposed method is proficiently compared with the state-of-the-art methods.&lt;/p&gt;</description>
  </descriptions>
</resource>
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