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Representation Learning.

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Bibliographic Details
Title: Representation Learning.
Authors: Schmidt, Benjamin (AUTHOR)
Source: American Historical Review. Sep2023, Vol. 128 Issue 3, p1350-1353. 4p.
Subjects: Machine learning, Historical research methods, Vector spaces, Artificial neural networks, Keyword searching, Optical character recognition
Abstract: The article discusses representation learning (RL), with a particular focus given to its use within the context of historical practice. Topics mentioned include vectors and vector spaces, artificial neural networks and the relationship between texts and images, full-text search, and optical character recognition (OCR).
Database: Psychology and Behavioral Sciences Collection
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PubType: Academic Journal
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PreciseRelevancyScore: 0
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  Data: Representation Learning.
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  Data: <searchLink fieldCode="JN" term="%22American+Historical+Review%22">American Historical Review</searchLink>. Sep2023, Vol. 128 Issue 3, p1350-1353. 4p.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Historical+research+methods%22">Historical research methods</searchLink><br /><searchLink fieldCode="DE" term="%22Vector+spaces%22">Vector spaces</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Keyword+searching%22">Keyword searching</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+character+recognition%22">Optical character recognition</searchLink>
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  Data: The article discusses representation learning (RL), with a particular focus given to its use within the context of historical practice. Topics mentioned include vectors and vector spaces, artificial neural networks and the relationship between texts and images, full-text search, and optical character recognition (OCR).
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=172362156
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1093/ahr/rhad363
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 4
        StartPage: 1350
    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Historical research methods
        Type: general
      – SubjectFull: Vector spaces
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Keyword searching
        Type: general
      – SubjectFull: Optical character recognition
        Type: general
    Titles:
      – TitleFull: Representation Learning.
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            – D: 01
              M: 09
              Text: Sep2023
              Type: published
              Y: 2023
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              Value: 128
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