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Predicting Spatial Data with Rotation Gradients and Machine Learning

De Pinho, Bruno


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  <identifier identifierType="DOI">10.5281/zenodo.889923</identifier>
  <creators>
    <creator>
      <creatorName>De Pinho, Bruno</creatorName>
      <givenName>Bruno</givenName>
      <familyName>De Pinho</familyName>
      <affiliation>Geology and Python</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Predicting Spatial Data with Rotation Gradients and Machine Learning</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>machine-learning, geophysics, geology, python, automl</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-09-12</date>
  </dates>
  <resourceType resourceTypeGeneral="InteractiveResource"/>
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    <rights rightsURI="info:eu-repo/semantics/restrictedAccess">Restricted Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Using Machine Learning (ML) algorithms to predict Airbourne Geophysics. A simple, but powerful solution using Rotation Gradients to add complexity to the model with a Automated Machine Learning (AutoML) implementation.&lt;/p&gt;</description>
  </descriptions>
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