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  <titleInfo>
    <title>Machine learning methods for commonsense reasoning processes</title>
    <subTitle>interactive models</subTitle>
  </titleInfo>
  <name type="personal">
    <namePart>Naidenova, Xenia</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
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  <typeOfResource>text</typeOfResource>
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  <originInfo>
    <place>
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    <place>
      <placeTerm type="text">Hershey, PA</placeTerm>
    </place>
    <publisher>Information Science Reference</publisher>
    <dateIssued>c2010</dateIssued>
    <dateIssued encoding="marc">2010</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">print</form>
    <extent>xiv, 410 p. : ill. ; 29 cm.</extent>
  </physicalDescription>
  <abstract>"The main purpose of this book is to demonstrate the possibility of transforming a large class of machine learning algorithms into integrated commonsense reasoning processes in which inductive and deductive inferences are not separated one from another but moreover they are correlated and support one another"--Provided by publisher</abstract>
  <tableOfContents>Knowledge in the psychology of thinking and mathematics -- Logic-based reasoning in the framework of artiticial intelligence -- The coordination of commonsense reasoning operations -- The logical rules of commonsense reasoning -- The examples of human connonsense reasoning processes -- Machine learning (ML) as a diagnostic task -- The concept of good classification (diagnostic) test -- The duality of good diagnostic tests -- Towards an integrative model of deductive-inductive commonsense reasoning -- Towards a model of fuzzy commonsense reasoning -- Object-oriented technology for expert system generation -- Case technology for psycho-diagnostic system generation -- Commonsense reasoning in intelligent computer systems</tableOfContents>
  <targetAudience authority="marctarget">specialized</targetAudience>
  <note type="statement of responsibility">Xenia Naidenova</note>
  <note>Includes bibliographical references (p. 400-401) and index</note>
  <subject authority="lcsh">
    <topic>Machine learning</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Correlation (Statistics)</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Recursive partitioning</topic>
  </subject>
  <identifier type="isbn">9781605668109 (hbk.)</identifier>
  <identifier type="isbn">9781605668116 (ebook)</identifier>
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    <recordIdentifier source="KUKTEM">vtls000054748</recordIdentifier>
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