1 Ocak 2012 Konferans bildirisi Açık Erişim

Irsoy, Ozan; Yildiz, Olcay Taner; Alpaydin, Ethem

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/86775</identifier>
  <creators>
    <creator>
      <creatorName>Irsoy, Ozan</creatorName>
      <givenName>Ozan</givenName>
      <familyName>Irsoy</familyName>
      <affiliation>Bogazici Univ, Dept Comp Engn, TR-34342 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Yildiz, Olcay Taner</creatorName>
      <givenName>Olcay Taner</givenName>
      <familyName>Yildiz</familyName>
      <affiliation>Isik Univ, Dept Comp Engn, TR-34980 Istanbul, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Alpaydin, Ethem</creatorName>
      <givenName>Ethem</givenName>
      <familyName>Alpaydin</familyName>
      <affiliation>Bogazici Univ, Dept Comp Engn, TR-34342 Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Soft Decision Trees</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2012</publicationYear>
  <dates>
    <date dateType="Issued">2012-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/86775</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.86774</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.86775</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">We discuss a novel decision tree architecture with soft decisions at the internal nodes where we choose both children with probabilities given by a sigmoid gating function. Our algorithm is incremental where new nodes are added when needed and parameters are learned using gradient-descent. We visualize the soft tree fit on a toy data set and then compare it with the canonical, hard decision tree over ten regression and classification data sets. Our proposed model has significantly higher accuracy using fewer nodes.</description>
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