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  <titleInfo>
    <nonSort>An </nonSort>
    <title>introduction to pattern recognition and machine learning / by Paul Fieguth</title>
  </titleInfo>
  <name type="personal">
    <namePart>Fieguth, Paul.</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <typeOfResource/>
  <originInfo>
    <place>
      <placeTerm type="text">Switzerland AG</placeTerm>
    </place>
    <publisher>Springer Nature</publisher>
    <dateIssued>2022</dateIssued>
    <edition>1st edition.</edition>
    <issuance/>
  </originInfo>
  <physicalDescription>
    <extent>xxii, 471 p. 24 cm.</extent>
  </physicalDescription>
  <abstract>This text offers an accessible and conceptually rich introduction, a solid mathematical development emphasizing simplicity and intuition. Students beginning to explore pattern recognition do not need a suite of mathematically advanced methods or complicated computational libraries to understand and appreciate pattern recognition; rather the fundamental concepts and insights, eminently teachable at the undergraduate level, motivate this text. This book provides methods of analysis that the reader can realistically undertake on their own, supported by real-world examples, case-studies, and worked numerical / computational studies.</abstract>
  <subject>
    <topic>Machine learning</topic>
  </subject>
  <subject>
    <topic>Pattern recognition</topic>
  </subject>
  <classification authority="udc">004.931'1 FIE</classification>
  <identifier type="isbn">9783030959937</identifier>
  <recordInfo>
    <recordChangeDate encoding="iso8601">20260902111140.0</recordChangeDate>
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