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Random Matrix Methods for Machine Learning - Romain Couillet - Bog

Bag om Random Matrix Methods for Machine Learning

"Numerous and large dimensional data is now a default setting in modern machine learning (ML). Standard ML algorithms, starting with kernel methods such as support vector machines and graph-based methods like the PageRank algorithm, were however initially designed out of small dimensional intuitions and tend to misbehave, if not completely collapse, when dealing with real-world large datasets. Random matrix theory has recently developed a broad spectrum of tools to help understand this new curse of dimensionality, to help repair or completely recreate the sub-optimal algorithms, and most importantly to provide new intuitions to deal with modern data mining"--

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  • Sprog:
  • Ukendt
  • ISBN:
  • 9781009123235
  • Indbinding:
  • Hardback
  • Sideantal:
  • 408
  • Udgivet:
  • 21. Juli 2022
  • Størrelse:
  • 173x24x247 mm.
  • Vægt:
  • 890 g.
  • 8-11 hverdage.
  • 10. April 2024
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Beskrivelse af Random Matrix Methods for Machine Learning

"Numerous and large dimensional data is now a default setting in modern machine learning (ML). Standard ML algorithms, starting with kernel methods such as support vector machines and graph-based methods like the PageRank algorithm, were however initially designed out of small dimensional intuitions and tend to misbehave, if not completely collapse, when dealing with real-world large datasets. Random matrix theory has recently developed a broad spectrum of tools to help understand this new curse of dimensionality, to help repair or completely recreate the sub-optimal algorithms, and most importantly to provide new intuitions to deal with modern data mining"--

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